Staff Machine Learning Engineer
$205,000–$272,500 year
Remote · United States or Boston, Massachusetts, United States
Job Summary
Define and execute multi-quarter technical roadmaps and system-level architectures for Motional’s large-scale ML platforms powering multimodal data discovery and autonomous-driving lifecycles. Architect scalable frameworks for massive data mining and real-time inference across GPU/CPU clusters; lead multi-person initiatives, mentor engineers, and influence department-level strategy. Drive cross-functional collaboration with Autonomy and ML teams, establish standards for ML system design, code quality, testing, incident response planning, and ensure production deployments meet latency and throughput targets. Apply a broad ML toolkit (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems and unblock teams; coach senior and junior engineers and elevate Motional’s engineering culture through documentation and design reviews.
Required Qualifications
- BS in Computer Science, Machine Learning, or related field (or equivalent practical experience)
- 8+ years of hands-on ML engineering experience
- Demonstrated experience with multimodal foundation models in ML production systems (integration, scaling, fine-tuning, deployment)
- Technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, driving department-level strategy
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX) and strong software engineering fundamentals (system design, CI/CD, containerization)
- Broad ML generalist knowledge spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
- Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
- Ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across teams
- Bonus points: MS/PhD, autonomous driving/robotics background, experience with massive-scale ML data mining, active learning, contrastive/representation learning, multimodal learning, sensor fusion, model serving tools, and MLOps platforms
- Willingness to work in a hybrid model with in-office locations in Boston, Pittsburgh, or Las Vegas or fully remote in the U.S.
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