Staff ML Engineer

About the role:

Samsara is the industry leader in AI for physical operations.

We’re hiring a Staff / Senior Staff Machine Learning Infrastructure Engineer to lead the design and evolution of our end-to-end ML platform powering Safety AI and adjacent product areas. This role combines deep platform ownership with direct product impact—enabling teams to build, deploy, and scale ML systems that improve real-world safety outcomes.

This is a remote position open to candidates based in Canada.

You should apply if:

• You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
• You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.
• You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
• You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
• You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.

• Design, build, and operate Samsara’s end-to-end ML platform spanning training, experimentation, batch and online inference, and edge deployment, used by multiple product teams across Safety AI and adjacent domains.
• Partner with product and applied ML teams to design, launch, and iterate ML-powered features (e.g., backend CV models, EcoDriving insights, LLM-based reporting), driving measurable improvements in safety outcomes, feature reliability, and cost efficiency.
• Lead throughput and cost estimation for new ML features—from early-stage exploration to production-scale capacity planning—informing roadmap and go/no-go decisions.
• Collaborate on experiment design and evaluation, including defining success metrics, structuring A/B tests or offline evaluations, and interpreting results to guide product and technical decisions.
• Evolve shared training and experimentation infrastructure (e.g., job orchestration, cluster configuration, environment management), and standardize experiment tracking, evaluation, and regression testing to enable fast and safe iteration.
• Design and operate scalable online and batch inference systems (Ray- and Spark-based), including deployment patterns, observability, and SLOs, while unifying training-to-production workflows and enabling consistent pipelines across teams.
• Partner with firmware and edge teams to define workflows for packaging, validating, and deploying models to Samsara devices, and build feedback loops from edge to cloud to support continuous improvement.
• Own the reliability, observability, and security posture of ML systems across cloud and edge environments, including on-call practices, incident response, and infrastructure hardening.
• Provide Staff+/Senior-Staff-level technical leadership by setting architecture and strategy for ML infrastructure, influencing cross-team decisions, and mentoring engineers and applied scientists.
• Drive strong developer experience through documentation, office hours, and best practices, while contributing to and representing Samsara in open source communities (e.g., Ray, Spark, RayDP).
• Own or co-own end-to-end technical delivery for high-priority or high-risk initiatives, from modeling and system design through production rollout.
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.

Minimum requirements for the role:

• 10+ years of overall experience in machine learning engineering or related fields, with a strong track record of building and operating large-scale ML systems.
• Strong experience with distributed computing frameworks such as Ray and/or Spark.
• Hands-on experience with cloud infrastructure (AWS), containers/Kubernetes, and production observability tooling.
• Proven experience building or supporting ML platforms (training, experimentation, or inference) used by multiple teams.
• Solid understanding of ML fundamentals including evaluation, experiment design, and model iteration in production environments.

An ideal candidate also has:

• Experience shipping ML-powered features end-to-end, from design through production and iteration, with measurable impact on product or business metrics.
• Background in computer vision and/or LLM-based systems in production environments.
• Experience with edge or on-device ML and collaboration with firmware or embedded teams.
• Familiarity with model lifecycle systems (model registry, deployment, monitoring, rollback, drift detection).
• Experience working in environments with strong security and compliance requirements.
• Demonstrated ability to lead across teams and influence technical direction at Staff+ scope.
• A strong sense of ownership and a desire for end-to-end autonomy—from platform design to real-world impact.