Senior Machine Learning Engineer - Platform
Description:
• Lead the transition from fragmented, task-specific models to a modular unified perception platform.
• Design and implement real-time ML systems for sensor ingestion, tracking, risk reasoning, and actuation.
• Integrate models across edge and cloud environments, including versioning, rollouts, and fallback mechanisms.
• Own end-to-end latency and reliability for safety-critical ML pipelines.
• Profile, schedule, and optimize messaging and backpressure across the stack.
• Build monitoring to detect model drift, false positives and negatives, and latency regressions.
• Develop evaluation frameworks for rare long-tail safety events and define ship/no-ship metrics and test sets.
• Partner with applied scientists to translate research outputs into production code that is performant, debuggable, and explainable.
• Shape system abstractions early to reduce technical debt and improve scalability.
• Mentor junior and mid-level engineers and help set standards for correctness and performance.
Requirements:
• 6+ years of experience in ML engineering with a track record of shipping production models.
• Experience in safety-critical domains such as robotics, automotive, or industrial AI is preferred.
• Deep understanding of distributed systems, performance profiling, and computer vision.
• Experience with cloud ML workflows on AWS, GCP, or Azure.
• Experience with containerization and an understanding of edge hardware constraints.
• Ability to design systems and evaluate trade-offs between model complexity and operational reliability.
• Ph.D. in Computer Science or a quantitative discipline such as Applied Math, Physics, or Statistics is preferred.
• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code frameworks is preferred.
• Experience deploying and managing ML applications in cloud environments and using cloud services for storage, processing, and inference is preferred.
• Experience building end-to-end ML applications from scratch is preferred.
Benefits:
• Annual base salary of $243,100 to $286,000 USD.
• Initial RSU grant with no vesting cliff.
• Ongoing refresh opportunities tied to performance, subject to plan terms and conditions.
• Above-market total compensation with base salary, performance-based bonus or variable pay, and equity for eligible roles.
• Flexible, employee-led remote working model.
• Professional development stipend.
• Comprehensive health plans.
• Comprehensive parental leave plans.
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• Lead the transition from fragmented, task-specific models to a modular unified perception platform.
• Design and implement real-time ML systems for sensor ingestion, tracking, risk reasoning, and actuation.
• Integrate models across edge and cloud environments, including versioning, rollouts, and fallback mechanisms.
• Own end-to-end latency and reliability for safety-critical ML pipelines.
• Profile, schedule, and optimize messaging and backpressure across the stack.
• Build monitoring to detect model drift, false positives and negatives, and latency regressions.
• Develop evaluation frameworks for rare long-tail safety events and define ship/no-ship metrics and test sets.
• Partner with applied scientists to translate research outputs into production code that is performant, debuggable, and explainable.
• Shape system abstractions early to reduce technical debt and improve scalability.
• Mentor junior and mid-level engineers and help set standards for correctness and performance.
Requirements:
• 6+ years of experience in ML engineering with a track record of shipping production models.
• Experience in safety-critical domains such as robotics, automotive, or industrial AI is preferred.
• Deep understanding of distributed systems, performance profiling, and computer vision.
• Experience with cloud ML workflows on AWS, GCP, or Azure.
• Experience with containerization and an understanding of edge hardware constraints.
• Ability to design systems and evaluate trade-offs between model complexity and operational reliability.
• Ph.D. in Computer Science or a quantitative discipline such as Applied Math, Physics, or Statistics is preferred.
• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code frameworks is preferred.
• Experience deploying and managing ML applications in cloud environments and using cloud services for storage, processing, and inference is preferred.
• Experience building end-to-end ML applications from scratch is preferred.
Benefits:
• Annual base salary of $243,100 to $286,000 USD.
• Initial RSU grant with no vesting cliff.
• Ongoing refresh opportunities tied to performance, subject to plan terms and conditions.
• Above-market total compensation with base salary, performance-based bonus or variable pay, and equity for eligible roles.
• Flexible, employee-led remote working model.
• Professional development stipend.
• Comprehensive health plans.
• Comprehensive parental leave plans.
Apply tot his job
Apply To this Job