Senior Machine Learning Engineer

About the Role

Safety AI builds the ML and computer vision systems behind Samsara's AI dash cameras, enabling real-time driver alerts, risk signals, and coaching insights running on millions of edge devices and the cloud.

This role owns the post-training lifecycle: building resilient, low-latency ML backend systems that turn static model artifacts into high-throughput, cloud-scale safety features. You will partner with applied scientists, firmware/full-stack engineers, and product managers to build ML APIs, data pipelines, and evaluation infrastructure that run Safety AI models at fleet scale.

Technical Charter & Impact

• Own the cloud-side path from model artifact to production system for Safety AI's ML applications.
• Establish practical standards for productionizing models—how they are served, evaluated, versioned, and monitored.
• Set a high bar for reliability: rigorous evaluation, measurable rollout health, and predictable failure modes.
• Act as a technical partner to applied scientists, helping translate research outputs into scalable, debuggable, and cost-efficient production systems.

Responsibilities

• Design Production ML APIs: Architect and maintain reliable, low-latency APIs to integrate Safety AI model outputs into cloud applications.
• Build Data Flywheels: Construct scalable pipelines to power continuous model iteration, backtesting, shadow, and online evaluation.
• Optimize & Serve Artifacts: Productionize model artifacts, optimizing serving logic and fine-tuning models for platform-specific workloads.
• Petabyte-Scale Operations: Process high-volume camera and sensor telematics data to support model execution, backtesting, and automated dataset curation.
• Monitor & Maintain Rollout Health: Build systems to track model drift, precision/recall, and latency regressions in production.
• Cross-Functional System Integration: Partner with firmware and platform teams to optimize edge-to-cloud model execution, balancing latency, throughput, and infrastructure cost.
• Product Collaboration: Work with product managers to translate safety requirements into scalable technical architectures.

Requirements

• 6+ years of experience as a Machine Learning Engineer or similar role, with a track record of shipping models in production.
• Strong proficiency in one or more common languages (e.g., C++, Go, Java, Python, Scala).
• Proficiency with common ML tools (e.g., Ray/Ray Serve, MLflow, Grafana, PyTorch, Spark).
• Experience deploying and iteratively refining models using real customer feedback loops.
• Comfort with full-stack/backend development—understanding data structures and dependencies underneath models.
• BS or MS in Computer Science or a related quantitative field.

Ideal Qualifications

• Ph.D. in Computer Science or a quantitative discipline (e.g., Applied Math, Physics, Statistics).
• Experience with containerization (Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code frameworks.
• Experience deploying and managing ML applications in cloud environments (AWS, GCP, Azure).
• Experience shipping end-to-end ML applications in safety-critical or high-scale domains.
• Expertise optimizing distributed model training with GPUs.