Job Description
The role involves leading the development and integration of machine learning systems for safety-critical applications within Samsara's Connected Operations Cloud, focusing on building scalable, reliable perception platforms and real-time decision-making infrastructure to improve physical operations across various industries.
Key Responsibilities
- Architect and lead the development of a unified perception platform supporting real-time safety systems.
- Design and implement real-time machine learning systems for sensor data ingestion, tracking, risk reasoning, and actuation.
- Manage model deployment across edge and cloud environments, including versioning, rollouts, and fallback mechanisms.
- Own end-to-end latency and reliability for safety-critical data pipelines.
- Develop monitoring systems to detect model drift, false positives/negatives, and latency issues, and incorporate feedback into model training.
- Create evaluation frameworks for safety cases, including metrics and test sets for rare safety events.
- Collaborate with applied scientists to ensure research outputs are production-ready, explainable, and debuggable.
- Shape system architecture early in platform development to ensure scalability and minimize technical debt.
- Mentor junior and mid-level engineers to uphold standards of correctness and performance.
Requirements
- Minimum of 6 years of experience in ML Engineering, with a proven track record of shipping models in production, ideally in safety-critical domains like robotics, automotive, or industrial AI.
- Deep understanding of distributed systems, performance profiling, and computer vision.
- Experience with Cloud ML workflows on AWS, GCP, or Azure, and containerization technologies such as Docker and Kubernetes, paired with an understanding of the constraints of edge hardware.
- Experience designing and implementing real-time ML systems from sensor ingestion and tracking to risk reasoning and actuation, ensuring clear interfaces and predictable system behavior.
- Experience orchestrating model integration across edge and cloud environments, managing versioning, rollouts, and fallback mechanisms.
- Ownership of end-to-end latency and reliability for safety-critical pipelines, including profiling, scheduling, and optimizing messaging and backpressure across the entire stack.
- Building sophisticated monitoring for deployed models to detect drift, false positives/negatives, and latency regressions, and closing the feedback loop to inform training iterations.
- Developing evaluation frameworks specifically for rare long-tail safety events, including defining metrics and building targeted test sets for safety cases.
- Partnering with Applied Scientists to ensure research outputs are translated into production code that is performant, debuggable, and explainable.
- Designing system abstractions early in the platform transition to minimize technical debt and maximize future scalability.
- Mentoring junior and mid-level engineers, setting standards for correctness and performance.
- Experience with deploying and managing ML applications in cloud environments, leveraging cloud-based services for data storage, processing, and inference.
- Experience building end-to-end ML applications from scratch.
Benefits & Perks
Competitive total compensation package including base salary, bonus, and equity (RSUs)
Remote and flexible working arrangements
Health benefits
Opportunities for career growth and mentorship
Inclusive work environment with accommodations for disabilities
Support for work-life balance through flexible work models
Ready to Apply?
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