Senior Machine Learning Engineer at Intuitive | Sunnyvale

About Applied IntuitionApplied Intuition, Inc. is at the forefront of physical AI innovation. Established in 2017 and currently valued at $15 billion, this Silicon Valley firm is developing the essential digital infrastructure required to integrate intelligence into every moving machine globally. Serving vital sectors such as automotive, defense, trucking, c…

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technology. Established in 2017 and currently valued at $15 billion, this Silicon Valley powerhouse is creating the essential digital framework that will infuse intelligence into every moving machine worldwide. Our solutions serve key sectors including automotive, defense, trucking, construction, mining, and agriculture, focusing on three main areas: tools and infrastructure, operating systems, and autonomy. Our trusted solutions are utilized by 18 of the top 20 global automakers and the United States military along with its allies. Our headquarters is located in Sunnyvale, California, with additional offices in Washington, D.C., San Diego, Ft. Walton Beach, Florida, Ann Arbor, Michigan, London, Stuttgart, Munich, Stockholm, Bangalore, Seoul, and Tokyo. Find out more at applied.co.We prioritize in-office collaboration, expecting our employees to work from their Applied Intuition office five days a week, while also embracing flexibility. Employees are trusted to manage their schedules, which may include occasional remote work, starting the day with morning meetings from home, or leaving early for family commitments.Hear from an Engineer:About the roleWe are searching for a talented software engineer specializing in perception for autonomous vehicles or mobile robotics. Your role will involve enhancing perception modules within our autonomous vehicle framework, including the development of 4D world representations to facilitate seamless autonomy. You will also lead the design and implementation of computer vision and machine learning strategies that empower self-driving vehicles to navigate effectively.In this dynamic and customer-centric team environment, you will not only contribute your engineering skills but also gain insights into best practices within the evolving autonomy sector. Our fast-paced culture encourages innovation and collaboration.

Join our innovative team at Wayve as a Machine Learning Engineer specializing in Application Software. In this pivotal role, you will leverage your expertise in machine learning algorithms and software development to create cutting-edge applications that drive our technology forward. Collaborate with a diverse group of talented professionals to enhance our products and deliver exceptional solutions that meet our clients' needs.

Cerebras Systems is at the forefront of AI technology, creating the world's largest AI chip that is 56 times the size of traditional GPUs. Our innovative wafer-scale architecture combines the compute power of dozens of GPUs into a single chip, simplifying the programming experience. This unique design enables us to achieve unparalleled training and inference speeds, allowing machine learning practitioners to run extensive ML applications seamlessly without the complexities of managing numerous GPUs or TPUs.Our clientele includes premier model laboratories, multinational corporations, and pioneering AI-driven startups. Notably, OpenAI has recently formed a multi-year collaboration with Cerebras, aiming to harness 750 megawatts of computational scale to revolutionize key workloads through ultra-high-speed inference.Thanks to our cutting-edge wafer-scale architecture, Cerebras Inference delivers the fastest Generative AI inference solution globally, achieving speeds over ten times faster than GPU-based hyperscale cloud inference services, thus transforming the user experience of AI applications and enabling real-time iterations and enhanced intelligence through additional agentic computation.Responsibilities:Lead the design and implementation of advanced system-level debugging, validation, and observability platforms.Develop automated systems for collecting and analyzing numerical data and execution anomalies.Create visualization and analysis tools to facilitate efficient root-cause investigations.Build frameworks for failure classification, regression detection, and anomaly monitoring.Enhance compilers, runtimes, and programming interfaces to support sophisticated profiling and instrumentation.Improve workflows related to system bring-up, low-level debugging, and validation.Collaborate cross-functionally with teams in compiler, hardware, firmware, runtime, and infrastructure domains.Establish best practices to ensure debuggability, reliability, and operational excellence.Lead impactful initiatives and support incident response while driving long-term corrective solutions.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI. Established in 2017 and currently valued at $15 billion, this Silicon Valley powerhouse is building the essential digital infrastructure to infuse intelligence into every moving machine globally. Our solutions cater to critical sectors, including automotive, defense, trucking, construction, mining, and agriculture, focusing on tools and infrastructure, operating systems, and autonomy. Trusted by 18 of the top 20 global automakers and the U.S. military and its allies, we are shaping the future of intelligent systems. Find out more at applied.co.We prioritize in-office collaboration, expecting our employees to work from our Applied Intuition office five days a week. Nevertheless, we value flexibility and trust our team to manage their schedules responsibly, which may include occasional remote work or adjusting hours to meet personal commitments.About the RoleWe are seeking a talented Software Engineer specializing in ML-first behavior prediction and planning. In this role, you will create advanced ML behavior modules capable of forecasting the future movements of road users and their interactions. You will collaborate closely with the perception team on training data generation and with the planning team on developing ML-based planners.Your contributions will not only enhance our engineering efforts but also immerse you in our vibrant, customer-centric team culture, where you will engage with industry best practices in the rapidly evolving autonomy sector. We pursue excellence in our products and operations, and if you are ready to make a significant impact in realizing autonomous systems, Applied Intuition is your ideal environment!

Cerebras Systems is at the forefront of AI innovation, having developed the world's largest AI chip, which is 56 times greater in size than conventional GPUs. Our revolutionary wafer-scale architecture delivers the computational power of multiple GPUs on a single chip, simplifying programming to a single device experience. This unique approach enables Cerebras to provide unparalleled training and inference speeds, allowing machine learning professionals to seamlessly operate large-scale ML applications without the complexities of managing numerous GPUs or TPUs.Our clientele includes leading model labs, global corporations, and pioneering AI-native startups. Recently, OpenAI formed a multi-year collaboration with Cerebras to harness 750 megawatts of capacity, revolutionizing critical workloads with ultra-fast inference capabilities.Thanks to our innovative wafer-scale architecture, Cerebras Inference stands as the fastest Generative AI inference solution globally, boasting speeds over ten times faster than traditional GPU-based hyperscale cloud inference services. This significant enhancement in speed transforms user experiences with AI applications, facilitating real-time iterations and augmenting intelligence through additional agentic computation.About The RoleIn the capacity of a Senior Software Engineer within the ML Integration and Quality team, you will be instrumental in integrating and delivering all software and hardware components of the Cerebras AI platform. Your focus will be on software feature integration and quality assurance, including pre-deployment and production validation of Cerebras' training and inference solutions. You will advocate for superior testing practices, effective debugging methodologies, and exemplary cross-team communication to ensure the delivery of world-class products.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI. Established in 2017 and currently valued at approximately $15 billion, this Silicon Valley-based company is dedicated to developing the digital infrastructure essential for instilling intelligence in every moving machine globally. We cater to various sectors, including automotive, defense, trucking, construction, mining, and agriculture, focusing on three main areas: tools and infrastructure, operating systems, and autonomy. Our solutions have earned the trust of 18 of the top 20 global automakers and the United States military and its allies. Headquartered in Sunnyvale, California, we have offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.We prioritize in-office collaboration and expect employees to work from their Applied Intuition office five days a week. However, we also value flexibility and trust our employees to responsibly manage their schedules, which may include occasional remote work, starting the day with morning meetings from home, or leaving earlier to accommodate family obligations.About the RoleWe are seeking infrastructure engineers with a strong background in machine learning pipelines, as well as ML engineers who are eager to extend their skills beyond modeling. This position will involve engagement across the complete ML lifecycle, including dataset generation, training frameworks, compute, evaluation, and deployment, collaborating closely with modeling teams. If you thrive on tackling broad and ambiguous challenges and enjoy working across the entire ML stack, this team is the perfect fit for you. At Applied Intuition, we encourage all engineers to take ownership of technical and product decisions, actively engage with both internal and external users to gather feedback, and contribute to a thoughtful, dynamic team culture.At Applied Intuition, You Will:Design and implement distributed cloud GPU training strategies for deep learning model training and evaluation.Create comprehensive machine learning pipelines and integrate them into our core systems.

Join Wayve Technologies as a Principal Machine Learning Engineer, where you will lead innovative projects that push the boundaries of application software development. You will collaborate with cross-functional teams to design, implement, and optimize machine learning algorithms that enhance our software solutions.

About the Institute of Foundation ModelsWe are an innovative research laboratory focused on the creation, comprehension, application, and risk management of foundation models. Our mission is to propel research forward, cultivate the next generation of AI innovators, and contribute significantly to a knowledge-driven economy.Joining our team presents a unique opportunity to engage in the core of advanced foundation model training, collaborating with leading researchers, data scientists, and engineers as we address the most pivotal and influential challenges in AI advancement. Your work will involve the creation of groundbreaking AI solutions with the potential to revolutionize entire industries. Employing strategic and innovative problem-solving skills will be crucial in establishing MBZUAI as a premier global center for high-performance computing in deep learning, fostering remarkable discoveries that inspire future AI trailblazers.

At Bee Genius, we are pioneering the future of work today with innovative AI solutions that transform industries.Job Overview: We are looking for a talented AI/Machine Learning Engineer to become a vital part of our dynamic team. In this role, you will leverage your expertise to develop and deploy cutting-edge machine learning models and algorithms aimed at addressing complex business challenges.Key Responsibilities:Design, build, and refine machine learning models and algorithms.Train and assess models using extensive datasets.Optimize models for enhanced performance and accuracy.Collaborate with data scientists and software engineers to integrate models into operational systems.Stay abreast of the latest trends in AI and machine learning technologies.Promote the ethical deployment of AI solutions.

Join our ETA Team at DoorDash as a Machine Learning Engineer. In this dynamic role, you will harness the power of machine learning to drive innovation and optimize our delivery processes. You will collaborate with cross-functional teams to develop and deploy scalable machine learning models that enhance customer experience and operational efficiency.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technology. Established in 2017 and currently valued at $15 billion, this Silicon Valley-based company is building the essential digital infrastructure to infuse intelligence into every moving machine worldwide. We cater to industries such as automotive, defense, trucking, construction, mining, and agriculture through three primary sectors: tools and infrastructure, operating systems, and autonomy. Our solutions are trusted by 18 of the top 20 global automakers, along with the United States military and its allies, to deliver exceptional physical intelligence. Our headquarters is located in Sunnyvale, California, with additional offices across Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.We are an in-office company, expecting our employees to primarily work from their Applied Intuition office five days a week. We understand the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.About the RoleWe are in search of a skilled software engineer with extensive experience in optimizing machine learning models and deploying them in production-grade embedded runtime environments. Your expertise will span the entire ML framework stack, including PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, and Triton.At Applied Intuition, You Will:Lead ML performance optimization across various technologies for both on-road and off-road ADAS/AD stacks aimed at deployment on a range of embedded computing platforms.Devise compute usage strategies to enhance efficiency and minimize latency of model inference for compute boards chosen by our customers.Engage in model pruning and quantization, ensuring successful deployment on memory-constrained platforms.Collaborate closely with ML engineers and software developers to identify and optimize efficient model architecture solutions.Establish methodologies to...

About the Institute of Foundation ModelsWe are a pioneering research laboratory focused on the development, understanding, application, and risk management of foundational models. Our mission is to propel research forward, cultivate the next generation of AI innovators, and make substantial contributions to a knowledge-driven economy.Join us and collaborate with top-tier researchers, data scientists, and engineers on the forefront of foundational model training. Engage in solving critical challenges that can redefine entire sectors through advanced AI solutions. Your strategic and innovative problem-solving skills will play a vital role in positioning MBZUAI as an international leader in high-performance computing for deep learning, facilitating discoveries that will inspire future AI trailblazers.The Role We are seeking a skilled distributed ML infrastructure engineer to enhance and expand our training systems. You will collaborate closely with distinguished researchers and engineers to:• Develop and scale distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)• Implement distributed optimizers based on mathematical specifications• Create robust configuration and launching systems across multi-node, multi-GPU clusters• Manage experiment tracking, metrics logging, and job monitoring for enhanced external visibility• Enhance the reliability, maintainability, and performance of training systems• While much of your work will support large-scale pre-training, prior pre-training experience is not mandatory; strong infrastructure and systems expertise are our primary focus.Key Responsibilities • Distributed Framework Ownership – Extend or adapt training frameworks (e.g., DeepSpeed, FSDP) to accommodate new applications and architectures.• Optimizer Implementation – Convert mathematical optimizer specifications into distributed implementations.• Launch Config & Debugging – Develop and troubleshoot multi-node launch scripts with adaptable batch sizes and parallelism strategies.

Illumio builds technology to contain ransomware and security breaches, helping organizations defend against cyber threats. The Illumio AI Security Graph underpins a platform that spots and contains threats in hybrid multi-cloud setups, aiming to stop attacks before they spread. Illumio is recognized as a leader in microsegmentation and supports Zero Trust architectures for critical infrastructure. The engineering team focuses on advancing cybersecurity through leadership, autonomy, and a strong sense of ownership. Engineers here develop and maintain a scalable SaaS platform using cloud-native tools, with deployments in both cloud and on-premises environments. Precision, quality, and collaboration shape the team's work, and engineers are encouraged to take initiative at every level. This Senior Machine Learning Engineer role is based onsite at Illumio’s Sunnyvale headquarters. The position centers on designing and scaling systems that power Illumio’s AI-driven security platform. Work involves handling large-scale data, distributed systems, and building advanced AI agents. Key Responsibilities Design and optimize high-throughput, event-driven systems with Apache Kafka to support real-time data flows. Develop and maintain large-scale data pipelines using Apache Spark or Flink for high-volume analytics and AI features. Create advanced AI agents that handle autonomous planning, memory management, and reliable tool use in distributed environments. Lead architectural design for containerized services on Kubernetes, focusing on availability and scalability across cloud platforms such as AWS, Azure, and GCP.

Your ImpactBecome a key member of our innovative team at Cylake Inc., where we are dedicated to designing cutting-edge cybersecurity solutions from the ground up. You will collaborate with industry experts to architect, develop, and launch impactful products that are set to revolutionize the cybersecurity landscape. This is an exciting opportunity to advance your career alongside a dynamic team committed to excellence.Role OverviewAs a Data Pipeline Engineer, you will be responsible for designing, constructing, and sustaining a robust, open-source data lakehouse architecture capable of supporting petabyte-scale analytics. Your role will involve creating end-to-end data pipelines that ensure seamless data ingestion, transformation, and consumption while maintaining high performance, reliability, and data integrity.

42dot is hiring a Senior AI Data Pipeline Engineer to support autonomous driving initiatives in Sunnyvale, United States. This position centers on building and refining data pipelines that power advanced vehicle systems. Role overview This engineer will design, implement, and optimize data workflows essential for autonomous vehicle technology. The work involves collaborating with teams across disciplines to ensure reliable and efficient data movement and processing. Key responsibilities Create and maintain data pipelines tailored for AI-driven vehicle systems Work closely with engineering and product teams to support system safety and performance Apply knowledge of AI and data engineering to improve pipeline reliability and scalability Requirements Experience in AI and data engineering Background in designing and optimizing data pipelines Ability to collaborate with cross-functional teams

Join our team at DoorDash as a Principal Machine Learning Engineer specializing in Ads and Promotions Delivery. In this pivotal role, you will leverage cutting-edge machine learning techniques to optimize our advertising strategies and enhance customer engagement. Collaborate with cross-functional teams to design and implement scalable machine learning models that drive business growth.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technologies. Established in 2017 and currently valued at $15 billion, our Silicon Valley-based company is dedicated to creating the essential digital infrastructure that empowers intelligence across all moving machines globally. Our solutions serve critical sectors including automotive, defense, trucking, construction, mining, and agriculture, focusing on three main domains: tools and infrastructure, operating systems, and autonomy. Trusted by 18 of the top 20 global automakers, as well as the U.S. military and its allies, Applied Intuition is committed to delivering unparalleled physical intelligence solutions. Our headquarters is located in Sunnyvale, California, complemented by offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. For more information, visit applied.co.We prioritize in-office collaboration and expect our employees to work primarily from our Applied Intuition office five days a week. However, we value flexibility and trust our team members to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving early when necessary to accommodate personal commitments.About the RoleAs an Engineering Manager on our Machine Learning Platform team, you will lead an exceptional group of engineers dedicated to building the infrastructure that enables Physical AI at scale. Your team will oversee three pivotal areas: Training & Inference Orchestration, where we develop frameworks to efficiently schedule and execute extensive tasks across thousands of GPUs; GPU Cluster Architecture, where we design and expand what will become the industry's largest GPU cluster for Physical AI; and Performance Optimization, where we maximize hardware utilization, throughput, and cost efficiency for large-scale training and inference workloads. You will collaborate at the intersection of systems engineering and machine learning, working directly with stack development and research teams to eliminate bottlenecks and expedite the transition from experimentation to production.

Cerebras Systems is at the forefront of AI innovation, creating the world’s largest AI chip, which is 56 times larger than traditional GPUs. Our groundbreaking wafer-scale architecture delivers the computational power equivalent to dozens of GPUs on a single chip, combined with the programming simplicity of a unified device. This innovative approach allows us to offer unparalleled training and inference speeds, enabling machine learning practitioners to execute extensive ML applications seamlessly, without the complexities of managing multiple GPUs or TPUs.Cerebras boasts an impressive clientele, including premier model labs, global corporations, and pioneering AI startups. Recently, OpenAI announced a multi-year partnership with Cerebras, aimed at deploying 750 megawatts of scale, revolutionizing critical workloads with ultra-fast inference capabilities.Our unique wafer-scale architecture enables Cerebras Inference to provide the fastest Generative AI inference solution globally, surpassing GPU-based hyperscale cloud inference services by more than tenfold. This remarkable enhancement in speed is reshaping the AI application user experience, facilitating real-time iteration and boosting intelligence through enhanced computational capabilities.About The RoleThe Inference ML Engineering team at Cerebras Systems is committed to empowering our rapid generative inference solution through intuitive APIs, supported by a distributed runtime that operates on extensive clusters of our proprietary hardware. Our goal is to enable enterprises, developers, and researchers to fully harness the capabilities of our platform, leveraging its exceptional performance, scalability, and flexibility. The team collaborates closely with cross-functional groups, including compiler developers, cluster orchestrators, ML scientists, cloud architects, and product teams, to deliver impactful solutions that redefine the limits of ML performance and usability.As a Senior Software Engineer on the Inference ML Engineering team, you will be instrumental in designing and implementing APIs, ML features, and tools that facilitate the execution of state-of-the-art generative AI models on our custom hardware. Your role will involve architecting solutions that allow for seamless model translation and execution, ensuring high throughput and minimal latency while maintaining user-friendliness. You will lead technical initiatives and collaborate with other engineering teams to enhance our solutions.

Join our innovative team at intuitive as a Machine Learning Engineer, where you'll have the chance to work on cutting-edge AI technologies that are shaping the future. In this role, you will design, develop, and implement machine learning models that will drive impactful solutions across various sectors.As a critical member of our team, you will collaborate with data scientists and engineers to enhance our product offerings, ensuring they are not only effective but also scalable. This is an exceptional opportunity for those eager to leverage their skills in a thriving environment.