Autonomy Engineer - Deep Learning Model Acceleration

At Skydio, the premier drone company in the US and a global leader in autonomous flight technology, we are shaping the future of drones and aerial mobility. Our team is composed of experts in artificial intelligence, innovative hardware and software development, operational excellence, and a strong commitment to customer satisfaction. We aim to broaden the accessibility of drone technology to a diverse range of users including utility inspectors, first responders, and military personnel.About the Position:The essence of our autonomy system lies in its ability to learn a semantic and geometric understanding of the world through visual data. We are at the forefront of developing real-time deep learning networks to advance intelligent mobile robotics. If you are passionate about utilizing vast structured video datasets to tackle challenges in Computer Vision (CV), such as object detection, tracking, optical flow estimation, and segmentation, we want to hear from you.As a Deep Learning Model Acceleration Engineer, you will play a pivotal role in constructing and scaling the infrastructure that underpins Skydio’s Deep Learning (DL) and AI initiatives. You will collaborate closely with our autonomy, embedded, and cloud teams to introduce new capabilities and support our deep learning efforts.Your Impact:Develop high-performance solutions for deep learning inference tailored to CV workloads, ensuring high throughput and low latency across various hardware platforms.Profile CV and Vision Language Models (VLMs) to assess performance, pinpoint bottlenecks, and discover optimization opportunities while enhancing the power efficiency of deep learning inference tasks.Design and implement comprehensive MLOps workflows for model deployment, monitoring, and retraining.Apply your advanced Machine Learning expertise to utilize training or runtime frameworks and model efficiency tools aimed at boosting system performance.Devise innovative methods to enhance training efficiency.Implement GPU kernels for custom architectures and optimized inference.Create SDKs enabling customers and external developers to construct autonomous workflows utilizing Machine Learning (ML).Leverage your knowledge and best practices to maintain and elevate Skydio’s engineering standards.Qualifications:Proven hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment.Strong foundational knowledge in deep learning frameworks and computer vision.Ability to work collaboratively in a fast-paced environment and manage multiple projects simultaneously.

Skydio stands at the forefront of drone technology as the leading US drone company and the global pioneer in autonomous flight—an essential aspect of the future of aerial mobility. Our team is comprised of experts in artificial intelligence, cutting-edge hardware and software development, operational excellence, and an unwavering commitment to customer satisfaction. We strive to empower a diverse range of drone users, from utility inspectors to first responders and beyond.About the Role:Our autonomy system relies on learning both semantic and geometric interpretations of the world through visual data. We are challenging the limits of real-time deep networks to enhance the capabilities of intelligent aerial robots, enabling them to autonomously navigate uncharted environments and deliver significant operational benefits. If you are passionate about applying deep learning in real-world scenarios and tackling complex issues in computer vision and autonomy, we would be thrilled to connect with you.In the role of Deep Learning Engineer, you will focus on training and deploying optimized models to tackle difficult challenges such as optical flow estimation, stereo depth estimation, object detection, segmentation and tracking, visual place recognition, localization and mapping, few-shot learning, occupancy networks, automated path planning, and more.Your Impact:Design, implement, and deploy advanced computer vision and multimodal deep learning models for Skydio's autonomy system.Utilize extensive real-world video and sensor data for data mining, curation, labeling, training, and evaluation.Employ large-scale and diverse synthetic data to enhance deep learning algorithms.Utilize state-of-the-art foundation models for knowledge distillation and efficient learning.Optimize models for low-latency performance on embedded hardware.Develop evaluation benchmarks and metrics to assess the performance of autonomous systems.Act as a versatile team member, contributing across various software aspects as needed.Qualifications:M.S. or Ph.D. in Computer Science, Electrical Engineering, or a related field.Proven hands-on experience in designing, training, and deploying deep learning models.Ability to produce high-quality, well-architected code (Python/PyTorch preferred).

Skydio stands at the forefront of autonomous flight technology, redefining the future of drones and aerial mobility. As the leading US drone manufacturer, our team blends unrivaled expertise in artificial intelligence with top-tier hardware and software development, operational excellence, and a commitment to customer satisfaction. We empower a diverse range of drone users, from utility inspectors and first responders to military personnel on the battlefield and beyond.About the Role:At Skydio, our autonomy system relies on advanced semantic and geometric comprehension of the world through visual data. We are pushing the limits of real-time deep learning networks to enhance intelligent mobile robotics. If you are passionate about utilizing vast structured video datasets to tackle challenges in Computer Vision (CV), including object detection, tracking, optical flow estimation, and segmentation, we want to connect with you.As a Deep Learning Infrastructure Engineer, you will play a pivotal role in constructing and scaling the infrastructure that underpins Skydio’s Deep Learning (DL) and AI initiatives. Collaborating closely with our autonomy, embedded, and cloud teams, you will deliver innovative capabilities and support the deep learning team.Your Impact:Develop high-performance solutions for deep learning inference tailored to CV workloads, ensuring high throughput and low latency across various hardware platforms.Analyze the performance of CV and Vision Language Models (VLMs) to pinpoint bottlenecks and opportunities for optimization and enhanced power efficiency.Design and implement comprehensive MLOps workflows for model deployment, monitoring, and retraining processes.Apply your advanced machine learning knowledge to improve system performance through training or runtime frameworks and model efficiency tools.Innovate new methods to enhance training efficiency.Develop GPU kernels for custom architectures and optimized inference.Create SDKs that facilitate customer and external developer engagement in building autonomous workflows utilizing Machine Learning (ML).Utilize your expertise and adherence to best practices to uphold and enhance Skydio’s engineering standards.Qualifications:Hands-on experience with MLOps, ML inference acceleration/optimization, and edge deployment.Proficiency in deep learning frameworks and tools.Strong understanding of computer vision principles and practices.Excellent problem-solving skills and attention to detail.

At Skydio, recognized as the premier US drone manufacturer and a global pioneer in autonomous flight technology, we are dedicated to revolutionizing the future of drones and aerial mobility. Our innovative team brings together profound expertise in artificial intelligence, state-of-the-art hardware and software product development, operational excellence, and an unwavering commitment to our customers. We aim to empower a diverse range of drone users, from utility inspectors and first responders to soldiers in challenging environments and beyond.About the Role:The essence of our autonomy system lies in learning a semantic and geometric understanding of the world through visual data. We are at the forefront of advancing real-time deep networks to accelerate the development of intelligent mobile robots. If you are passionate about tackling complex challenges in object detection, tracking, motion prediction, flow estimation, and comprehensive scene understanding using vast amounts of structured video data, we eagerly invite you to join our team.Your Impact:Design and implement cutting-edge deep learning solutions that address detection, tracking, segmentation, and optical flow estimation tasks in real-time on Skydio drones.Utilize advanced techniques in unsupervised learning, few-shot learning, and foundational models to enhance data-efficient machine learning.Curate and enhance synthetic datasets that fuel our deep learning algorithms alongside extensive structured video data.Refine and optimize models for low-latency performance on embedded hardware.Characterize and evaluate the performance of our vision systems.Research and prototype innovative methodologies.Act as a generalist, contributing across various aspects of the software when necessary.Ideal Candidate:Proven hands-on experience in developing and deploying deep learning models.Experience in curating both synthetic and real-world image datasets.Solid foundation in software engineering with a commitment to writing clean, well-structured code (preferably in Python and C++).Demonstrated ability to prototype, train, optimize, and deploy deep neural networks.Strong ability to comprehend and contextualize scientific papers and literature in computer vision.Thrives in a fast-paced, collaborative, small team environment.

Skydio builds autonomous flight technology for drones used in fields like utilities, public safety, and defense. The Zurich team blends artificial intelligence with hardware and software development, delivering smart aerial systems for real-world missions. Skydio drones support a range of professionals, including utility inspectors, first responders, and soldiers in tactical scenarios. Internship focus This Autonomy Engineer Intern position centers on deep learning for computational photography in autonomous drones. Skydio’s autonomy system relies on advanced semantic and geometric understanding, using high-quality visual data. The Zurich team develops real-time deep learning networks that help drones navigate new environments and deliver valuable data for inspection, mapping, ISR, and public safety tasks. Interns will contribute to computational photography models and algorithms, aiming to improve camera performance and support a variety of drone missions. The work spans imaging, computer vision, and autonomy, applying deep learning to practical scenarios. What you will do Design, implement, and deploy deep learning models for computational photography, including super-resolution, multi-frame denoising, low-light imaging, and HDR imaging. Handle large sets of real-world video and sensor data for data mining, curation, labeling, training, and evaluation. Train and deploy optimized models to tackle challenges such as multimodal scene understanding, optical flow and range estimation, object detection, segmentation and tracking, visual localization and mapping, few-shot learning, and automated path planning. Location This internship is based in Zurich, Switzerland.

Intrinsic Robotics is at the forefront of redefining industrial robotics as part of Alphabet's innovative vision. We are committed to harnessing advances in AI, perception, and simulation to unlock new possibilities in the field of industrial robotics, with software and data at the heart of our solutions. Our mission is to make industrial robotics smarter, more accessible, and user-friendly for a diverse range of businesses, entrepreneurs, and developers. Our dynamic team consists of engineers, roboticists, designers, and technologists who are passionate about unleashing the creative and economic potential of industrial robotics. Role OverviewAs a Senior Research Scientist within our Applied Research team, you will spearhead applied research initiatives and design groundbreaking algorithms in geometric computer vision and image/video-centric foundation models tailored for scalable deployment in industrial robotics. Your expertise in Deep Learning will aid in defining processes and formulating strategies to reduce hallucination risks, enhance accuracy, and execute algorithms on industrial robotics workcells, thereby assessing the sim-to-real transfer of models. Additionally, you will guide the development of synthetic data and annotations essential for training foundation models, identifying gaps in the sim-to-real transfer process. Collaboration with research partners and interdisciplinary engineering teams will be crucial as we explore new methodologies and architectures to integrate cutting-edge foundation models into real-world industrial applications. Continuous learning and embracing unfamiliar technologies will be integral to your role. Your ImpactYour contributions will be pivotal in democratizing access to advanced AI capabilities within the robotics sector, boosting the reliability of deploying AI models at scale in industrial environments. Your day-to-day responsibilities will include: You will demonstrate insight and innovation, proactively identifying challenges and proposing practical solutions that can be implemented in high-throughput settings.You will act as a liaison between the robotics team and our research partners, collaborating effectively to provide engineering insights into research projects and conceptualize next-generation solutions. You will serve as an advisor to engineering and cross-functional teams, guiding them in translating early-stage AI research into robust engineering capabilities within Flowstate (Intrinsic’s robotics software platform).

Skydio stands at the forefront of autonomous flight technology, revolutionizing the future of drones and aerial mobility. As the leading US drone company, we harness cutting-edge artificial intelligence, state-of-the-art hardware, and exceptional software development to empower a diverse range of drone users, from utility inspectors to first responders and military personnel.About the Role:Join us in building the world’s most sophisticated autonomous drones utilized in various sectors including inspection, public safety, defense, and cinematography. Your innovative research will not just remain theoretical; it will take flight, directly influencing how pilots and operators execute missions in challenging environments. You will develop and implement reinforcement learning and related policy-learning methodologies that enhance the ability of Skydio aircraft to autonomously plan, navigate, and control themselves more intelligently, safely, and efficiently across our ecosystem, which includes handheld applications, ground control systems, cloud autonomy services, and fleet operations.How You'll Make an Impact:Adaptive Navigation & Avoidance: Train adaptive policies for navigating complex 3D environments (such as forests, urban areas, and bridges), enhancing our existing obstacle avoidance and goal-seeking capabilities.Reinforcement Learning-Enhanced Planning: Integrate learned cost functions and value assessments with trajectory optimization techniques to ensure smooth, agile flight while adhering to safety parameters and mission requirements.Simulated to Real-World Application: Create scalable datasets and training loops leveraging Isaac Lab, domain randomization, and safety protocols, with weekly validation on actual drones.Human-in-the-Loop Control: Develop assistive policies that seamlessly merge pilot intent with autonomous operations, enhancing the ease of control transitions.Collaborative Fleet Operations: Investigate decentralized coordination strategies for effective coverage, pursuit, and collaborative mapping with minimal communication.What Sets This Internship Apart:Hands-On Hardware Experience: Utilize simulations for prototyping and conduct flight tests on production-ready aircraft and edge computing systems. Your contributions will have a tangible impact beyond the simulation environment.Emphasis on Safety: We prioritize safety in our learning processes by integrating formal constraints, uncertainty estimation, and conservative action filters to ensure reliable deployment in human-centric environments.

About Flexion Robotics Flexion Robotics is building the intelligence framework for tomorrow’s humanoid robots. The company’s mission is to move from early prototypes to fully functional humanoid systems. Founded by leading scientists in robot reinforcement learning (with backgrounds at Nvidia and ETH Zürich) and backed by top international venture capital, Flexion Robotics has quickly progressed from first lines of code to deploying real humanoid capabilities. Role Overview The Machine Learning Infrastructure Engineer will help shape the core computing and data systems that support cognitive development for humanoid robots. This position focuses on building and maintaining the platforms needed to train large foundational models on substantial datasets. The work involves designing training clusters, architecting data pipelines to move information from simulators and robots into model training, and creating tools that enable AI engineers to train, evaluate, and iterate efficiently. This is a senior, on-site position based in Zürich. The Infrastructure team includes engineers with experience at Google, Meta, and Amazon. The role offers broad responsibility for systems supporting data collection, training, and experimentation workflows, including infrastructure strategy, cluster orchestration, distributed training, data platforms, CI, and experimentation tools. What You Will Do Design, deploy, and maintain GPU compute clusters for large-scale model training across multiple cloud providers, including job scheduling with Slurm and Kubernetes. Build data platforms and pipelines: set up storage, processing, and serving layers to manage data from simulator outputs and robot telemetry to training datasets. This includes infrastructure using object storage (S3), parallel filesystems (Lustre), and data formats such as Parquet, WebDataset, and LeRobot. Use distributed processing tools like Ray and Spark to transform and validate data at scale. Work with AI engineers to optimize distributed training on multi-node GPU clusters, focusing on throughput, device utilization, and communication efficiency. Improve distributed IsaacLab-based sim-to-real training workflows. Evaluate and select new platforms: assess cloud providers, GPU-as-a-Service options, and new tools, taking ownership of decisions as computing needs grow. Location This role is on-site at Flexion Robotics’ Zürich office.

Join Us as a Senior Machine Learning Engineer (Computer Vision) At Scandit, we empower individuals with innovative technology that enhances efficiency across various industries. From optimizing delivery operations to ensuring accurate medication delivery, our solutions automate workflows and provide critical insights. Be part of our journey as we innovate and expand, taking Scandit to new heights. Role Overview We are seeking a talented Senior Machine Learning Engineer to join our Label Capture team. In this role, you will work on developing digital twins of intricate labels and identification documents, enhancing our existing product offerings by implementing new features and optimizing current functionalities. You will utilize cutting-edge computer vision and deep learning algorithms to significantly improve information extraction from printed materials, surpassing existing standards. If you are excited by these challenges, we want to hear from you! Key Responsibilities Lead the semantic document parsing initiatives for customer applications. Engage in all stages of quality enhancement, including metrics definition, prototyping, testing, model integration, and data analysis. Keep abreast of the latest developments in document classification, text recognition, and information extraction. Drive innovation by identifying areas for enhancing recognition capabilities. Mentor fellow engineers on code quality and best engineering practices. About You You are a seasoned ML engineer with a passion for designing, deploying, and refining machine learning systems. With over five years of experience, you have successfully delivered advanced computer vision solutions, both in cloud environments and on-device, from initial prototype to production. You understand that software development combines technology with human interaction, allowing you to thrive in both independent and collaborative settings. While you stay informed about emerging trends in CV/ML, you appreciate the value of established technologies and incremental improvements. Your creativity and determination drive you to tackle real-world challenges innovatively, and you excel at taking ownership and navigating obstacles efficiently. You possess a keen eye for detail, balancing precision with pragmatic approaches to achieve our objectives. If this resonates with you, we look forward to your application!

Role Overview Destinus Group is seeking a Machine Learning Engineer in Zürich to help build advanced computer vision systems for autonomous flight. This role focuses on developing neural networks that must perform reliably in complex, safety-critical environments outside the lab. The work combines deep learning, simulation, and rigorous verification to support real-world autonomous systems where failure is not an option. Destinus Group, through its AI and autonomy hub Daedalean AG, is advancing high-speed, unmanned aerial systems for defense applications. The team works at the intersection of research and aviation, enabling autonomous machines to perceive, understand, and decide in real time. This position offers the chance to contribute directly to next-generation flight technologies that support critical missions worldwide. What You Will Do Design, train, and improve deep learning models for computer vision in dynamic, safety-critical settings. Manage the full machine learning workflow: data strategy, model architecture, deployment, and performance monitoring. Develop evaluation frameworks to ensure models meet strict reliability and safety requirements. Participate in machine learning certification, validating models within regulatory standards. Apply transfer learning and simulation to expand training and testing beyond available real-world data. Work closely with software, systems, and flight teams to integrate machine learning into operational systems. Continuously refine models to handle edge cases, varied environments, and operational constraints.

Skydio stands at the forefront of the drone industry, recognized as the premier US drone company and global leader in autonomous flight technology, which is vital for the future of aerial mobility. Our team is comprised of experts in artificial intelligence, innovative hardware and software development, operational excellence, and an unwavering commitment to customer satisfaction. We empower a diverse range of drone users, from utility inspectors and first responders to military personnel on the battlefield and beyond.About the Role:We invite applications for a seasoned software engineer to spearhead the creation of tools that enhance each phase of the autonomy lifecycle. You will play a pivotal role in developing and refining essential internal platforms that enable engineers to explore new concepts, analyze system behaviors, and comprehend intricate interactions between software and the physical environment.This position merges backend systems development with frontend visualization, necessitating a robust understanding of software design, developer experience, and a genuine passion for empowering colleagues through effective tooling. The systems you create will be utilized daily by autonomy developers, test engineers, and various other stakeholders within the organization.

Visium is looking for a Junior Machine Learning Engineer Intern to join the team in Zürich. This internship offers hands-on experience with artificial intelligence and machine learning projects, working closely with experienced engineers. Role overview As a Junior Machine Learning Engineer Intern, you will contribute to building and refining machine learning models. The work includes supporting the development, testing, and deployment of models that support different projects within the company. What you will do Assist in designing and developing machine learning models Support testing and validation of model performance Help deploy models to production environments Work with team members to solve real-world problems using data-driven approaches Team and environment This role involves close collaboration with experienced professionals in a supportive and learning-focused setting. Interns are encouraged to ask questions and contribute ideas as part of the team’s ongoing projects.

At Exa, we are revolutionizing the landscape of search engines by constructing a robust platform from the ground up to cater to every AI application. Our team is dedicated to building a colossal infrastructure that efficiently crawls the web, trains cutting-edge embedding models for indexing, and develops high-performance vector databases using Rust for swift searching capabilities. We are proud to operate a $5M H200 GPU cluster that powers tens of thousands of machines with unparalleled efficiency.As a member of our Machine Learning team, you will play a pivotal role in training foundational models for advanced search functionalities. Our mission is to create systems capable of instantaneously filtering the vast knowledge available online to deliver exactly what you need, no matter the complexity of your query. Essentially, we aim to transform the web into an extraordinarily powerful database.We are in search of a passionate Machine Learning Research Engineer who will focus on training embedding models to enhance web search accuracy. This role will involve conceptualizing innovative transformer-based architectures for search, generating extensive datasets, conducting evaluations, and consistently striving to surpass our internal state-of-the-art benchmarks.

Title: Senior Machine Learning Engineer / Data ScientistType: Full-timeLocation: ZürichAbout UsAt Visium, we empower enterprise leaders to define their AI and data strategies, execute large-scale transformations, and implement AI across operations to ensure their organizations are future-ready. With deep expertise in strategy, architecture, cloud engineering, analytics, artificial intelligence, and machine learning, we enable our clients to harness and scale their data effectively.Our mission is to forge a brighter future by building ethical, future-proof organizations. Join our community of innovators, dreamers, and doers who strive to create a world we can all be proud of – our Visiumees.Are you ready to join us?Role OverviewAs a key member of our expanding team, you will contribute to our growth in Switzerland and beyond. In your role as a Senior Machine Learning Engineer, you'll engage in diverse applied research projects utilizing cutting-edge machine learning techniques and assist in deploying solutions that enhance customer experiences and adoption. Your passion for understanding the business context behind features will be essential, and your meticulous organization will enable you to thrive in our fast-paced, dynamic team environment.

WHO WE AREAt Proxima Fusion, we are on a transformative journey to revolutionize sustainable energy. Our pioneering concept, centered around the advanced W7-X stellarator and cutting-edge technology, aims to create commercially viable fusion power plants.Our innovative work in stellarator optimization, enhanced by state-of-the-art computational tools and machine learning, is pushing the boundaries of fusion technology. We are unlocking new, high-performance design parameters through the application of high-temperature superconducting magnets.To seize this monumental opportunity, we are assembling a team of highly dedicated and passionate individuals committed to driving extraordinary advancements and radically transforming technology on a global scale.WHY JOIN PROXIMA FUSIONImpact: Your simulations will play a crucial role in shaping the magnets that support the future of commercial fusion energy.Ownership: As a member of a small, expert team, you will establish modeling standards and significantly influence key design decisions.Frontier Engineering: Engage at the cutting edge of high-field electromagnetics, cryogenics, and sophisticated numerical methods.Collaboration: Join forces with a team that combines profound superconducting expertise with advanced computational capabilities to tackle one of the most formidable engineering challenges of our era.YOUR IMPACTAt Proxima Fusion, we are pioneering the design of the first generation of fusion power plants to deliver clean, carbon-free energy to the world. The core of our reactor is its superconducting coils, which operate at cryogenic temperatures, generate intense magnetic fields, and must maintain stability amid complex electromagnetic and thermal transients.We are seeking a Numerical Modeling Engineer to create high-fidelity simulation tools that predict and mitigate the behavior of our superconducting magnets. Your work will encompass electromagnetic, thermal, and transient multiphysics modeling, including quench dynamics, and will directly inform the design choices for conductors, coils, and protection systems.This position emphasizes the creation of robust numerical frameworks, integrating commercial multiphysics tools with in-house developed models to enable prompt, reliable, physics-driven engineering decisions.

Duration: Minimum 6 months; ideally 9–12 months, depending on the candidate’s experience.At Scandit, we empower individuals with cutting-edge technology. Whether it’s accelerating delivery routes for drivers, ensuring patients receive the correct medications, or streamlining retail operations, our solutions transform workflows and yield actionable insights across diverse sectors. Join us as we innovate, expand, and elevate Scandit to new heights.About the InternshipThis research-oriented internship is designed to propel advancements in machine learning techniques for intricate visual understanding tasks. You will engage in projects that focus on deep learning frameworks for image-to-sequence modeling, including Transformers and attention mechanisms, to tackle complex and highly structured computer vision challenges. Your contributions will play a crucial role in our long-term research initiatives targeting enhanced performance, robustness, and generalization in large-scale visual applications.Your ResponsibilitiesCollaborating closely with seasoned ML researchers and engineers, you will be at the forefront of groundbreaking research that intersects computer vision and sequence modeling. Your key responsibilities will include:Designing and testing innovative ML architectures for structured visual data.Assessing various modeling paradigms, including encoder-decoder frameworks and hybrid Transformer models.Exploring methods to enhance robustness, generalization, and multi-view reasoning capabilities.Conducting systematic experiments, ablation studies, and error analyses to substantiate your research hypotheses.This internship offers a unique opportunity for original model development, extensive experimentation, and impactful scholarly work. You will help drive technological innovation with potential real-world implications for millions of users, making this position ideal for master’s students, PhD candidates, or those aspiring for research careers in academia or industry.Your ProfileYou are a Master's or PhD student in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline, with a strong emphasis on research. You possess a solid understanding of machine learning theory, neural networks, and computer vision.Key Skills:Expertise in Python and deep learning frameworks such as PyTorch.Hands-on experience in designing, training, and evaluating neural networks, including CNNs and Transformer architectures.Exceptional analytical and problem-solving skills, with the ability to tackle complex challenges.

About GenPeach AIGenPeach AI is a pioneering research lab dedicated to developing vertical multimodal foundation models aimed at creating hyper-realistic human representations in images and videos. Our mission is to empower human creativity through advanced AI tools rather than replace it.We build our models from the ground up, utilizing proprietary datasets at an expansive scale, innovative architectures and training methodologies, extensive GPU resources, and seamless product integration to expedite the delivery of our research to end users.Our team consists of approximately 10 highly skilled professionals, guided by advisors from Google DeepMind and supported by prominent AI-focused investors and advisors from OpenAI, Meta AI, Microsoft AI, Project Prometheus, and Fal. Collectively, our team and advisors have significantly contributed to groundbreaking models such as Meta’s Imagine/MovieGen, OpenAI’s Sora, Google’s Veo, and Gemini.About the TeamYou will become a key member of the research team, focused on advancing image/video generation and multimodal understanding. Collaborating closely with fellow Research Engineers, Scientists, and Founders, you will transform innovative research into scalable training processes, robust evaluations, and production-ready systems.About the RoleWe are seeking an AI Research Engineer to contribute to the end-to-end development and scaling of GenPeach’s foundational models. Your responsibilities will include the implementation of new model concepts and training methodologies, managing critical aspects of the training stack that influence quality and efficiency, and navigating production constraints.This role is hands-on with a high degree of ownership, where you will write research-quality code that is vital for production.In this role, you willDevelop and refine image/video generative model concepts (architecture, loss functions, conditioning, sampling, distillation, post-training adjustments)Oversee training performance comprehensively (distributed training, throughput, memory management, stability, debugging scaling issues)Establish and enhance the experimental workflow (evaluations, ablation studies, reproducibility tools, reporting, decision-making processes)Create and optimize VLMs for image/video captioning (data preparation, training strategies, model variations, evaluation)Conduct high-frequency research: review literature as needed, implement concepts, and validate findings empirically

Flexion Robotics develops intelligence for next-generation humanoid robots, aiming to move quickly from prototypes to real-world use. Founded by specialists in robot reinforcement learning from organizations like Nvidia and ETH Zürich, the company has progressed from early code to functional humanoid systems, supported by international venture capital. Role overview This AI Research Engineer position centers on reinforcement learning for manipulation in humanoid robots. The work blends research and engineering, with a focus on enabling robots to perform long-horizon, contact-rich tasks reliably outside the lab. The role involves significant ownership and close collaboration with simulation and control teams to deliver integrated robotic capabilities. What you will do Advance RL-based manipulation: Design and implement learning strategies for complex, dexterous manipulation tasks, moving humanoid robots toward practical deployment. Address long-horizon and sparse-reward challenges: Develop algorithms and systems to help robots handle multi-stage tasks with delayed or sparse rewards, tackling issues like credit assignment, exploration, and stability in real-world conditions. Facilitate sim-to-real transfer: Create methods for transferring policies from simulation to physical robots, focusing on robustness, managing model mismatches, and applying techniques such as domain randomization and system identification. Enhance RL systems: Optimize large-scale training pipelines for learning across many parallel environments, and work with simulation teams to improve efficiency and enable more complex behaviors. Innovate learning methodologies: Explore and apply new approaches that combine reinforcement learning, imitation learning, and model-based methods, going beyond standard practices when needed. Location This role is based in Zürich, Zurich, Switzerland.

Join our dynamic team at On Running AG as a Specialist in Anaplan Solution Architecture and Model Building. You will play a critical role in designing and implementing robust Anaplan models that drive strategic business decisions and enhance operational efficiency.

At Scandit, we empower individuals with cutting-edge technology that transforms everyday tasks into efficient workflows. Our innovative solutions enhance operational efficiency across various sectors, enabling quicker deliveries, accurate medication matching, and streamlined retail operations. Join us in our mission to elevate Scandit to new heights through continuous growth and innovation. About the Internship We are seeking enthusiastic students who are passionate about computer vision and machine learning to become part of our Zürich team for a duration of four to six months. As an intern, you will contribute to product development by improving and extending algorithms and their automated testing infrastructure. This internship presents an exceptional opportunity to apply your academic knowledge while gaining valuable hands-on experience in a collaborative international environment. What You Will Do Depending on your background and interests, you may work on a variety of exciting projects, including: Refining barcode scanning algorithms to perform effectively in challenging conditions such as deformed surfaces, glare, or reflections. Enhancing our real-time visual tracking and pose estimation systems by integrating advanced sensor measurements and state-of-the-art machine learning algorithms. Improving our object and context detection capabilities through the development and integration of cutting-edge machine learning techniques. Advancing our real-time visualization systems to facilitate the analysis and development of our computer vision algorithms. Streamlining our image annotation tools by implementing intelligent algorithms to reduce manual efforts. Creating features for image classification, matching, and reconstruction to enhance our algorithms for monitoring shelf and product conditions in supermarkets. Our Tech Stack Computer vision algorithms: C++17, CMake Machine learning training and tools, cloud processing: Python, Pytorch Acceleration: SIMD, Vulkan, CoreML