Senior ML Engineer

### Role Overview 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. ### Key Responsibilities 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. 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. 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. 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 performant, debuggable, and explainable. 4. Technical Leadership - Strategic Influence: Shape system abstractions early in the platform transition to minimize technical debt. - Mentorship: Set engineering standards for correctness and performance while mentoring junior and mid-level engineers. ### Minimum Requirements - 6+ years of experience in ML Engineering with a proven track record of shipping models in production (robotics, automotive, or industrial AI). - Deep understanding of distributed systems, performance profiling, and computer vision. - Experience with Cloud ML workflows (AWS/GCP/Azure) and containerization, paired with an understanding of edge hardware constraints. - Architectural Mindset: Ability to design systems and understand trade-offs between model complexity and operational reliability.