Senior Machine Learning Engineer - Platform

About the role:

We are looking for a Senior Machine Learning Engineer to lead the architectural evolution of our safety systems. You will move our ML stack from siloed, end-to-end models toward a unified Perception Platform Layer. Your mission is to build the robust infrastructure that translates raw sensor data into real-time, high-stakes decisions, ensuring our models perform reliably across both cloud and edge environments.

This is a remote position for candidates based in the US.

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.

1. Platform Architecture & Unification

• Architect a Unified Perception Layer: Lead the transition from fragmented, task-specific models to a modular perception platform that supports reusable components and downstream safety applications.
• System Design: Design and implement real-time ML systems—from sensor ingestion and tracking to risk reasoning and actuation—ensuring clear interfaces and predictable system behavior.
• Hybrid Deployment: Orchestrate model integration across edge and cloud environments, managing versioning, rollouts, and mission-critical fallback mechanisms.

2. Performance & Reliability Engineering

• Latency Ownership: Own end-to-end latency and reliability for safety-critical pipelines. You will profile, schedule, and optimize messaging and backpressure across the entire stack.
• Observability & Feedback Loops: Build sophisticated monitoring for deployed models to detect drift, false positives/negatives, and latency regressions. You will "close the loop" to ensure production data informs the next iteration of training.

3. Rigorous Evaluation & Safety

• Safety Cases: Develop evaluation frameworks specifically for rare "long-tail" safety events. You will define metrics and build targeted test sets that form the basis for principled ship/no-ship decisions.
• Explainability: Partner with Applied Scientists to ensure research outputs are translated into production code that is not only performant but also debuggable and explainable.

4. Technical Leadership

• Strategic Influence: Shape the system abstractions early in the platform transition to minimize technical debt and maximize future scalability.
• Mentorship: Set the engineering standard for correctness and performance. You will mentor junior and mid-level engineers, fostering a culture of rigorous ML engineering.

Minimum requirements for the role:

• Experience: 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).
• Systems Mastery: Deep understanding of distributed systems, performance profiling, and computer vision.
• Infrastructure Fluency: Experience with Cloud ML workflows (AWS/GCP/Azure) and containerization, paired with an understanding of the constraints of edge hardware.
• Architectural Mindset: You don't just write code; you design systems. You understand the trade-offs between model complexity and operational reliability.

An ideal candidate also has:

• Ph.D. in Computer Science or quantitative discipline (e.g., Applied Math, Physics, Statistics)
• Experience with containerization technologies (e.g., Docker, Kubernetes), continuous integration/continuous deployment (CI/CD) pipelines, and infrastructure-as-code (IaC) frameworks
• Familiar with deploying and managing ML applications in cloud environments, as well as leveraging cloud-based services for data storage, processing, and inference
• Experience building end-to-end ML applications from scratch