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Salary: USD 170,170 - 286,000 / annual
Samsara (NYSE: IOT) is the pioneer of the Connected Operations Cloud, a platform enabling organizations to harness IoT data from physical operations—agriculture, construction, field services, transportation, and manufacturing—to develop actionable insights and improve safety, efficiency, and sustainability.
The Safety AI team builds ML and computer vision systems behind Samsara's AI dash cameras, which deliver real-time driver alerts, risk signals, and coaching insights running on millions of edge devices and in the cloud. This role owns the production ML backend systems that transform static model artifacts into high-throughput, cloud-scale safety features.
You will partner closely with applied scientists, firmware engineers, full-stack engineers, and product managers to build the ML APIs, data pipelines, and evaluation infrastructure that enable Safety AI models to run efficiently at fleet scale. Your work closes the loop from initial integration through rollout monitoring and iteration, delivering trustworthy, customer-facing signals. This is ML engineering where the stakes are real: rare, high-consequence events, millions of vehicles, and a product where "it works" means someone got home safely.
Key responsibilities include:
- 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're served, evaluated, versioned, and monitored
- Set a high bar for reliability: rigorous evaluation, measurable rollout health, and systems that degrade predictably
- Act as a technical partner to applied scientists, translating research outputs into debuggable, scalable, cost-efficient production systems
- Design production ML APIs: architect and maintain reliable, low-latency APIs integrating Safety AI model outputs into cloud applications
- Build data flywheels: construct scalable pipelines powering continuous model iteration, backtesting, shadow and online evaluation
- Optimize and serve artifacts: productionize model artifacts, optimizing serving logic and fine-tuning for platform-specific workloads
- Process petabyte-scale camera and sensor telematics data supporting model execution, backtesting, and automated dataset curation
- Monitor and maintain rollout health: build systems tracking model drift, precision/recall, and latency regressions in production
- Partner with firmware and platform teams to optimize edge-to-cloud model execution, balancing latency, throughput, and cost
- Work with product managers to translate safety requirements into scalable technical architectures
- Champion Samsara's cultural principles as the company scales globally
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 (C++, Golang, Java, Python, Scala)
- Proficiency with common ML tools (Ray/Ray Serve, MLflow, Grafana, PyTorch, Spark, etc.)
- 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 candidates also have:
- Ph.D. in Computer Science or a quantitative discipline (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 from artifact to production, ideally in safety-critical or high-scale domains
- Expertise optimizing distributed model training with GPUs
