Staff Software Engineer- CAM

Details

• 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.
• Define and drive the technical strategy for CAM’s predictive maintenance platform, including architecture for data ingestion, feature engineering, model serving, and customer-facing workflows.
• Lead the design and implementation of new predictive models and data products, working closely with ML/data partners to move from proof-of-concept to robust, monitored, and continuously improving production systems.
• Own and evolve large-scale, distributed systems that process high-volume time-series and event data from Samsara devices and third-party sources, ensuring reliability, performance, and cost efficiency as we grow.
• Collaborate across the stack (backend, web, and potentially mobile) to deliver end-to-end features—APIs, data models, business logic, and intuitive UI flows—that bring predictive insights into day-to-day maintenance workflows.
• Partner with product, design, and customer-facing teams to define the roadmap, translate ambiguous business problems into clear technical projects, and measure the impact of what we ship (e.g., reduced breakdowns, fewer emergency repairs, improved shop throughput).
• Mentor and multiply other engineers on the team through code reviews, design reviews, technical coaching, and by setting high standards for quality, reliability, and velocity.
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• 8+ years of experience in software design and development, including building and operating production systems at scale.
• 3+ years building data-intensive or ML-backed products, such as forecasting systems, anomaly detection, recommendation systems, or other high-complexity predictive models (e.g., in fintech, health, reliability, or similar domains).
• Strong programming fundamentals and deep proficiency in Python for backend and data/ML-related services.
• Experience designing and operating distributed systems or large-scale microservices (e.g., event-driven architectures, time-series storage, streaming or batch data pipelines).
• Demonstrated experience leading cross-team or cross-org projects from inception through rollout, including managing ambiguity, driving alignment, and delivering measurable business impact.
• Strong communication skills and the ability to translate between technical and non-technical stakeholders, especially when discussing trade-offs around reliability, performance, and model accuracy.
• Proven ability to "sit with the customer" to deeply understand operational pain points, translating vague real-world maintenance challenges into high-impact technical requirements and intuitive AI-driven products.
• Master’s degree in Computer Science + Artificial Intelligence.
• Expertise with time-series data and forecasting techniques, including feature engineering, evaluation, and productionization for predictive maintenance or similar use cases.
• Hands-on experience with MLOps practices and tooling (e.g., model deployment, monitoring, versioning, experimentation platforms) in partnership with data/ML teams.
• Experience with full-stack development using technologies such as Go, TypeScript/JavaScript, React, GraphQL, or similar modern stacks in large-scale SaaS applications.
• Background working with industrial, fleet, or equipment data (e.g., telematics, fault codes, work orders, inspections) and an interest in learning the maintenance domain in depth.
• Proven track record of mentoring senior engineers and influencing engineering culture, standards, and best practices beyond a single team.