Senior/Staff Software Engineer for Machine Learning Data

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating as the Google Self-Driving Car Project in 2009, Waymo has developed the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and save thousands of lives currently lost to traffic accidents. The Waymo Driver operates Waymo’s fully autonomous ride-hail service and is adaptable to various vehicle platforms and use cases. With over ten million rider-only trips, our technology has autonomously driven more than 100 million miles on public roads and processed tens of billions of miles in simulation across more than 15 U.S. states.The Waymo ML Frameworks & Efficiency team collaborates closely with Research and Production teams to design and implement models in Perception and Planning, which are essential components of our autonomous driving software. We provide our partners with the most effective frameworks for the entire model development lifecycle and innovative efficiency solutions for model execution, tailored to the unique challenges of machine learning in autonomous driving.We are seeking talented engineers with expertise in ML frameworks or ML systems to enhance computational efficiency in both cloud environments and vehicle systems. You will engage with the entire ML stack, from deep learning model architectures to accelerator runtimes, working alongside ML modeling teams to drive large-scale efficient model training and inference.

At Waymo, we are pioneering the future of autonomous driving technology with an unwavering commitment to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, our focus has always been on engineering the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and drastically reduce traffic-related fatalities. The Waymo Driver currently powers our fully autonomous ride-hail service, having completed over ten million rider-only trips and covering more than 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.This role in Software Engineering is crucial as we develop the intelligent systems that allow the Waymo Driver to navigate complex environments, make sound decisions, and ensure safe transportation for our users. We tackle intricate technical challenges in robotics, perception, decision-making, and deep learning, collaborating closely with hardware and systems engineers. If you are a dedicated software engineer or researcher with a passion for Level 4 autonomous driving, we are eager to meet you.In this hybrid role, you will report directly to a Technical Lead Manager.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world’s most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has been relentlessly focused on creating the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and prevent traffic-related fatalities. The Waymo Driver is the driving force behind our fully autonomous ride-hail service, optimized for various vehicle platforms and applications. With over ten million rider-only trips and more than 100 million miles driven autonomously on public roads, alongside tens of billions of simulated miles across 15+ U.S. states, Waymo is redefining transportation.The ML Platform team at Waymo plays a critical role by offering a suite of tools that facilitate and automate the entire machine learning workflow lifecycle, including feature and experiment management, model development, optimization, and monitoring. Our initiatives have made machine learning more accessible across diverse teams at Waymo, including Perception, Planner, Research, and Simulation.We are seeking talented engineers with expertise in machine learning software or systems to enhance compute performance both in the cloud and on vehicles. You'll engage with the entire ML stack from a systems perspective, tackling challenges such as efficient deep learning models, model compression, and improving ML software (e.g., JAX, XLA, Triton, and CUDA). This hybrid position reports directly to the Senior Manager of Runtime and Optimization.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Established in 2009 as the Google Self-Driving Car Project, Waymo has developed the Waymo Driver—The World’s Most Experienced Driver™—aimed at enhancing mobility access and saving countless lives from traffic-related accidents. Our technology empowers a fully autonomous ride-hail service and is adaptable across various vehicle platforms and product applications. With over ten million rider-only trips facilitated by the Waymo Driver and more than 100 million miles driven autonomously on public roads, we are paving the way for safer transportation.The Perception team is responsible for developing systems that interpret the spatial-temporal representations and semantic meanings of the environment surrounding our autonomous vehicles. Our collaborative efforts with downstream teams focus on optimizing and integrating these systems within the Waymo Driver. We engage in innovative research to tackle real-world challenges and work closely with research teams at Alphabet. Our engineers have access to extensive driving data from diverse sensors, allowing us to (1) create efficient learning methods from vast real-world datasets, (2) build and train models at scale, (3) analyze real-world behaviors, and (4) optimize models for both onboard and offboard hardware.In this hybrid role, you will report to a Technical Lead Manager.

Join Waymo as a Senior Staff Machine Learning Engineer specializing in Depot Automation, where you will play a vital role in transforming the future of transportation through cutting-edge machine learning solutions.Your expertise will drive innovation and efficiency, ensuring our autonomous vehicles operate seamlessly in various environments. Collaborate with a team of passionate engineers and researchers to develop and implement advanced algorithms that enhance our depot operations.

Waymo is a pioneering autonomous driving technology firm dedicated to becoming the world’s most reliable driver. Originating as the Google Self-Driving Car Project in 2009, Waymo has committed itself to developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access while aiming to save thousands of lives lost in traffic accidents. The Waymo Driver underpins Waymo’s fully autonomous ride-hailing services and can be adapted across various vehicle platforms and applications. With more than ten million rider-only trips completed, it has driven over 100 million miles on public roads and tens of billions of miles in simulations across 15+ U.S. states.In this hybrid role, you will work closely under the guidance of a Technical Lead in Planner Technology.Your Responsibilities:Analyze driving behavior issues from logs to identify underlying causes.Enhance the performance of cutting-edge machine learning models in the Planner.Build foundational frameworks to further the capabilities of the Waymo Driver.Broaden the influence of machine learning models while maintaining interpretability and behavioral assurances.Contribute to solving some of the most complex driving scenarios.

About UsAt Aurora, our mission is to advance the future of transportation by delivering self-driving technology that is safe, efficient, and accessible to all.Discover The Aurora Driver - a revolutionary platform that aims to transform mobility and logistics, enhancing road safety and accessibility.Joining Aurora means collaborating with a team of passionate experts who tackle complex challenges and continuously strive for excellence. To stay updated with our latest innovations, visit aurora.tech or follow us on LinkedIn.Aurora is committed to building a diverse team of talented individuals dedicated to creating a safer transportation ecosystem and improving the efficiency of goods delivery.We are currently seeking a highly motivated and experienced Staff Software Engineer to join our Autonomy Frameworks team. In this pivotal role, you will contribute to the development of our core robotics middleware that powers the Aurora Driver. Your work will focus on optimizing the performance, determinism, and efficiency of our Autonomy and Simulation applications. This is a unique opportunity to make a significant impact on the safety and reliability of our self-driving technology.

Join our dynamic team at Moveworks as a Senior Staff Machine Learning Engineer. In this pivotal role, you will leverage your expertise in machine learning and artificial intelligence to develop and enhance agentic systems that transform the way organizations interact with their customers. You will work alongside a talented group of engineers and data scientists to create innovative solutions that improve user experiences.

About UsNuro is at the forefront of self-driving technology, dedicated to making autonomy available to everyone. Established in 2016, we are developing the world's most scalable driver by merging innovative AI with robust automotive hardware. Our flagship technology, the Nuro Driver™, is licensed to facilitate a variety of applications, from robotaxis to commercial fleets and personal vehicles. With years of successful self-driving deployments, Nuro is paving the way for automakers and mobility platforms to achieve commercial-scale autonomy, enabling a safer, more interconnected future.Role OverviewOur Sensor Data and Calibration team is expanding, and we are seeking a Senior Software Engineer with a strong background in robotics and machine learning. You will be responsible for designing both online and offline unstructured (targetless) sensor calibration algorithms. The ideal candidate will have extensive hands-on experience in the research, development, and application of machine learning methods utilizing various sensor data types (IMU, camera, lidar, radar, etc.). Familiarity with classical state estimation techniques, such as non-linear least-squares optimization and Lie algebra, is a plus.Key ResponsibilitiesDevelop cutting-edge state estimation and calibration algorithms leveraging both traditional robotics and modern machine learning techniques.Evaluate and characterize the accuracy and performance metrics of state estimation and calibration algorithms.Integrate calibration pipelines into the autonomy system while monitoring onboard performance and resource usage.Collaborate with cross-functional teams to enhance ML model training and overall performance assessment.Address critical inquiries regarding sensor data and overall autonomy effectiveness.

About Us Nuro is a pioneering self-driving technology company committed to making autonomy accessible for everyone. Established in 2016, we are developing the world’s most scalable autonomous vehicle, integrating advanced AI with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed to enable a variety of applications, including robotaxis, commercial fleets, and personal vehicles. With a proven track record in self-driving deployments, Nuro provides automakers and mobility platforms a clear pathway to large-scale autonomous vehicles, paving the way for a safer, more connected future. Position OverviewOur robotics division is expanding, and we are on the lookout for an experienced Software Engineer to join our Sensor Data and Calibration team. We seek a candidate with a strong background in robotics and machine learning to create synthetic sensor simulation models and algorithms. The ideal applicant will possess hands-on experience with research, development, and implementation of machine learning techniques (such as NeRF or Gaussian splatting) aimed at generating synthetic sensor data, including photorealistic images and realistic lidar and/or radar outputs. Your Responsibilities Research, develop, and implement cutting-edge synthetic sensor simulation methodologies. Evaluate and characterize the authenticity and utility of synthetic sensor data. Address critical inquiries regarding sensor data and the performance of autonomous systems. Collaborate with cross-functional teams to establish mapping needs and specifications. Candidate Profile One of the following qualifications: PhD in machine learning, computer science, electrical engineering, robotics, or a related discipline with at least 3 years of industry experience. Master's degree with a minimum of 4 years of industry experience. 5+ years of relevant industry experience. In-depth understanding of machine learning principles, with practical experience in training and evaluating modern ML models. Proficient in Python, with familiarity in deep learning frameworks like PyTorch, TensorFlow, or Jax. Desired Skills Comprehensive knowledge of 3D geometry and state estimation concepts. Strong programming skills in systems coding. Experience in simulating/modeling real sensors (camera, lidar, radar, IMU, etc.), including noise modeling. Familiarity with contemporary ML graphics techniques, such as NeRF, Gaussian Splatting, and/or generative models.

Coupang is hiring a Senior Staff II, Machine Learning Engineer (Tech Lead) for the Search team in Mountain View, USA. This position centers on building and enhancing the core technology behind search for one of South Korea’s largest e-commerce platforms. Role overview This role focuses on designing, developing, and refining machine learning systems that directly impact how millions of people shop online. As a technical lead, expect to work on projects that shape the search experience and improve the accuracy and speed of results for Coupang’s customers. Team and culture Coupang encourages teams to launch new initiatives and solve complex problems. Employees regularly see the results of their work, both in personal growth and in the company’s progress. The culture blends the drive of a startup with the stability and resources of a global public company. What makes this role unique Work on technology that influences how millions shop online. Join a team that prioritizes innovation and ongoing improvement. Contribute to a company known for transforming e-commerce in South Korea. Experience the energy of a startup with the support of a global organization. This is a chance to help define the future of commerce as part of Coupang’s technology-driven Search team.

Join Waymo as a Tech Lead Manager for Machine Learning Optimization, where you will spearhead innovative projects to enhance our self-driving technology. In this role, you will lead a team of talented engineers and data scientists, guiding the development of advanced algorithms and optimization techniques that drive performance and reliability. You will collaborate with cross-functional teams to ensure the successful integration of ML models into our systems, pushing the boundaries of autonomous vehicle technology.

Waymo is a pioneering autonomous driving technology company dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, our focus has been on developing the Waymo Driver—The World’s Most Experienced Driver™. Our mission is to enhance mobility access while saving lives that are currently lost to traffic accidents. The Waymo Driver facilitates our fully autonomous ride-hail service and is adaptable to a variety of vehicle platforms and product use cases. To date, we have completed over ten million rider-only trips, driven over 100 million miles on public roads, and performed tens of billions of miles in simulation across more than 15 U.S. states.The Waymo ML Frameworks & Efficiency team collaborates with both Research and Production teams to create core models in Perception and Planning essential to our autonomous driving software. We empower our partners by providing optimal frameworks throughout the model development lifecycle, encompassing both pre-training and post-training phases. Our frameworks are designed to efficiently scale models while addressing the unique challenges of machine learning in autonomous driving.We invite skilled engineers with expertise in machine learning systems to join us in refining and enhancing pre-trained models for deployment within the Waymo Driver and future products. You will collaborate with researchers and modeling engineers across the organization to tackle the complexities of large-scale reinforcement learning (RL), developing systems capable of scaling across various computational, data, and environmental contexts to enhance model intelligence and interpret human driving behaviors.

About the RoleJoin our dynamic team of experts where machine learning and systems engineering intersect to enhance the performance of autonomous systems. As a Senior Software Engineer specializing in Perception Machine Learning Data, you will play a crucial role in integrating machine learning advancements with autonomy infrastructure, ensuring that our models are trained using the most pertinent, diverse, and high-quality datasets. Your contributions will significantly influence how autonomous systems recognize uncommon scenarios, adapt to various geographical contexts, and operate safely at scale.Key Responsibilities Include:Utilizing Vision Language Models (VLMs) to compile diverse datasets that reflect real-world driving patterns across different regions.Creating high-fidelity synthetic data frameworks across multiple sensor modalities.Enhancing machine learning-powered validation processes for data quality and model preparedness.Your Impact:High-Output Generalist: Collaborate across various domains including autonomy, infrastructure, databases, simulation, and machine learning development, while expanding your expertise in Robotics and ML.Robotics Specialist: Develop cutting-edge solutions for data discovery, automated labeling, and synthetic data generation in close cooperation with the Infrastructure and Autonomy teams.About the WorkTackle the most demanding data challenges in autonomy by applying machine learning and rigorous systems engineering principles:Design hybrid systems that combine deep learning with traditional algorithms for scalable data curation and annotation.Create frameworks to evaluate the real-world authenticity of synthetic data and enhance the quality of synthetic data rendering.Develop tools to automatically identify data gaps that affect the performance of perception models.Collaborate with autonomy engineers to transform raw sensor data into prioritized training objectives, addressing critical gaps that hinder perception and autonomy performance.About YouBachelor’s degree in Computer Science, Robotics, Statistics, Physics, Mathematics, or a related quantitative field.Experience:4+ years of professional software engineering experience, proficient in Python and familiar with C/C++. Demonstrated ability to lead cross-functional technical projects from conception to execution.You have hands-on experience in implementing machine learning solutions and enjoy embedding them into practical systems. Your focus is on delivering impactful, integrated solutions rather than solely theoretical ML projects.Bonus PointsExperience working with synthetic or autonomous driving data.Background in building machine learning systems for robotic applications.

Role Overview Unity Technologies is advancing mobile gaming with AI-driven features. The Principal Machine Learning Engineer will focus on deploying advanced AI models, such as transformers and diffusion networks, directly onto mobile devices. This position shapes how Unity brings state-of-the-art multi-modal models from research into real-world mobile applications. What You Will Do Technical Leadership: Set the vision for deploying multi-modal AI models on iOS and Android, drawing on deep experience with transformers, diffusion models, and generative architectures. Make key decisions on model optimization strategies, including compression, quantization, and knowledge distillation to address mobile device constraints. Assess and select inference runtimes (such as CoreML, ONNX Runtime Mobile, TFLite) to improve team capabilities and deployment outcomes. Oversee the entire optimization pipeline, from model export through hardware-specific kernel tuning across different processing units. Architecture & Research Translation: Work closely with research scientists to convert innovative model architectures into operational, mobile-optimized systems. Design scalable systems capable of processing varied inputs, images, text, metadata, while ensuring real-time output performance. Develop new approaches for dynamic resolution and token reduction tailored for mobile environments. Monitor and incorporate advancements in efficient AI technologies to keep Unity’s mobile AI stack current. Team Leadership & Mentorship: Guide and mentor machine learning engineers, establishing best practices for on-device performance evaluation. Collaborate with cross-functional teams to ensure AI capabilities align with product roadmaps and device requirements. Promote a culture centered on performance measurement, defining and tracking key metrics for efficiency and accuracy. Location Mountain View, CA, USA

About Us Nuro is a pioneering self-driving technology firm focused on democratizing autonomy for everyone. Established in 2016, we are on a mission to create the world's most scalable autonomous driver. Our innovative approach blends advanced AI with automotive-grade hardware, allowing us to license our core technology, the Nuro Driver™, across diverse applications including robotaxis, commercial fleets, and personal vehicles. Our proven technology, tested through numerous self-driving deployments, provides automakers and mobility platforms with a clear path to commercial-scale autonomous vehicles, fostering a safer, more connected future. Position OverviewWe are excited to expand our robotics team and are seeking a talented Senior Software Engineer to join our Sensor Data and Calibration unit. The ideal candidate will possess a strong background in robotics and machine learning, with a focus on developing sophisticated synthetic sensor simulation models and algorithms. Experience in research, development, and application of machine learning techniques (such as NeRF or Gaussian splatting) for generating synthetic sensor data, including photorealistic images and realistic LiDAR or radar outputs, is essential. Key Responsibilities Research and create cutting-edge synthetic sensor simulation methodologies. Evaluate and characterize the realism and effectiveness of synthetic sensor data. Address essential inquiries regarding sensor data and autonomous performance. Collaborate with various teams across autonomy, infrastructure, and systems to define mapping requirements. Qualifications One of the following: PhD in machine learning, computer science, electrical engineering, robotics, or a related field, with 3+ years of industry experience. Master's degree with 4+ years of industry experience. 5+ years of industry experience. In-depth knowledge of machine learning fundamentals with practical experience in training and assessing contemporary ML models. Proficient in Python, with experience in deep learning frameworks such as PyTorch, TensorFlow, or JAX. Preferred Qualifications Strong grasp of 3D geometry and state estimation principles. Experience in systems-level coding. Familiarity with simulating and modeling real sensors (camera, LiDAR, radar, IMU, etc.), including noise modeling. Expertise in modern ML graphics techniques, such as NeRF, Gaussian Splatting, and/or generative models.

About Us Nuro is at the forefront of self-driving technology, striving to make autonomous driving accessible to everyone. Established in 2016, we are dedicated to developing the world’s most scalable driver by merging advanced artificial intelligence with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed across various applications, including robotaxis, commercial fleets, and personal vehicles. With proven technology from years of successful self-driving deployments, we provide automakers and mobility platforms with a clear pathway to commercial-scale autonomous vehicles, paving the way for a safer, more connected future. Role Overview At Nuro, we prioritize a machine-learning-centric approach to autonomous driving. The performance of our systems relies significantly on the volume and diversity of training and evaluation data. Our team is vital in enhancing autonomous driving systems by ensuring high-quality labeled data accessibility. This is achieved through a sophisticated labeling stack that includes a workflow execution framework, supporting infrastructure, and an array of data annotation tools. Our autonomy stack is powered by an industry-leading sensor suite, capable of efficiently processing and annotating millions of sensor data points weekly. The platform team's mission is to ensure that labeled data is readily available for all users. Our systems must be dependable and scalable, encompassing everything from request submissions to progress tracking, data dumping, and model development. We work closely with autonomy engineers to guarantee the labeled data's quality and comprehensiveness. As Nuro expands its services, it becomes increasingly critical to capture and promptly triage all issues. Key Responsibilities Develop and maintain highly available and fault-tolerant systems for data annotation. Productionize core infrastructure for our cutting-edge autonomy system. Enhance label quality through data-driven metrics and monitoring. Leverage state-of-the-art ML research to automate and optimize the data labeling lifecycle. Qualifications Bachelor's or Master's degree in a relevant field with 5+ years of industry experience. Proven experience in designing, building, and operating highly scalable and reliable distributed data systems. Demonstrated ability to lead cross-functional projects with strong communication skills. Expertise in data processing and machine learning techniques.

Join Waymo as a Staff Software Engineer specializing in Simulation Capacity Optimization. In this pivotal role, you will leverage your expertise in software engineering and simulation technology to enhance our autonomous vehicle systems. Your contributions will be vital in optimizing the performance and efficiency of our simulations, ensuring that our vehicles are equipped to navigate the complexities of real-world environments.

Who We AreAt Ema, we are pioneering cutting-edge AI technologies designed to enhance the creativity and productivity of employees across enterprises. Our unique technology enables organizations to entrust Ema, the AI employee, with repetitive tasks, freeing up human talent for more strategic initiatives. Founded by a team of former executives from tech giants such as Google, Coinbase, and Okta, and backed by prestigious investors including Accel Partners and Naspers, we are on the forefront of the AI revolution.Our diverse team, composed of top engineers from leading tech firms like Microsoft Research and Facebook, brings a wealth of knowledge and experience, primarily from renowned institutions such as Stanford and MIT. Operating out of Silicon Valley and Bangalore, India, we embrace a hybrid work model, requiring employees to be in our Mountain View, CA office three days a week.Who You AreWe seek passionate and innovative Machine Learning Engineers who thrive on tackling complex challenges and enjoy working with vast datasets. You have a talent for translating theoretical concepts into practical, scalable solutions, and you are a collaborative team player who excels in autonomous settings. Your enthusiasm for employing machine learning techniques, particularly in Natural Language Processing and Information Retrieval, is matched only by your desire to contribute to a mission-driven, high-growth startup making a significant impact.Your ResponsibilitiesDesign, develop, and deploy machine learning models that drive our NLP, retrieval, ranking, reasoning, dialog, and code-generation systems.Implement state-of-the-art machine learning algorithms, including Transformer-based models and reinforcement learning, to enhance AI system performance.Analyze and process large, complex datasets (structured, semi-structured, and unstructured) to inform model development.Engage throughout the entire machine learning model lifecycle, from problem identification and data exploration to feature engineering and model evaluation.