{"schemaVersion":"jobsearcher.job.v1","id":"722b9b11440835905d3ced76","url":"https://jobsearcher.com/jobs/722b9b11440835905d3ced76","canonicalUrl":"https://jobsearcher.com/jobs/722b9b11440835905d3ced76","title":"Machine Learning Engineer","description":"About the role\r\nWe are seeking a high-impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles.\r\nYou will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.\r\nThis role is onsite 5 days a week at our Santa Clara, CA office!\r\nWhat you'll doEnd-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.\r\nAutonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.\r\nEfficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints.\r\nReal-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.\r\nModel Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.\r\nSimulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.\r\nScalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.\r\nData Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.\r\nCross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.What we're looking forEducation: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.\r\nExperience: Open to all experience levels. Leveling will be determined based on experience and technical depth.\r\nProgramming & Frameworks:\r\nStrong Python skills and experience with frameworks such as PyTorch or TensorFlow.\r\nStrong C++ skills and experience integrating ML models into high-performance production systems.\r\nCore ML & Systems Expertise:\r\nDeep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.\r\nExperience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.\r\nExperience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.\r\nExperience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.\r\nInfrastructure & Compute Tools:\r\nExperience with CUDA and TensorRT is highly desirable.\r\nExperience with cloud-based ML training and evaluation pipelines, preferably Azure.Bonus Qualifications:Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.\r\nExperience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.\r\nPublications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.\r\nPrior contributions to large-scale ML systems deployed in production.Salary Range\r\n$170,000 - $240,000\r\n#J-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"Santa Clara","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-09-23T01:26:03.026Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"About the role\r\nWe are seeking a high-impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles.\r\nYou will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.\r\nThis role is onsite 5 days a week at our Santa Clara, CA office!\r\nWhat you'll doEnd-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.\r\nAutonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.\r\nEfficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints.\r\nReal-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.\r\nModel Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.\r\nSimulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.\r\nScalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.\r\nData Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.\r\nCross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.What we're looking forEducation: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.\r\nExperience: Open to all experience levels. Leveling will be determined based on experience and technical depth.\r\nProgramming & Frameworks:\r\nStrong Python skills and experience with frameworks such as PyTorch or TensorFlow.\r\nStrong C++ skills and experience integrating ML models into high-performance production systems.\r\nCore ML & Systems Expertise:\r\nDeep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.\r\nExperience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.\r\nExperience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.\r\nExperience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.\r\nInfrastructure & Compute Tools:\r\nExperience with CUDA and TensorRT is highly desirable.\r\nExperience with cloud-based ML training and evaluation pipelines, preferably Azure.Bonus Qualifications:Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.\r\nExperience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.\r\nPublications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.\r\nPrior contributions to large-scale ML systems deployed in production.Salary Range\r\n$170,000 - $240,000\r\n#J-18808-Ljbffr","datePosted":"2026-09-23T01:26:03.026Z","dateModified":"2026-09-23T01:26:03.026Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Santa Clara","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"722b9b11440835905d3ced76"},"url":"https://jobsearcher.com/jobs/722b9b11440835905d3ced76"}}