Senior Machine Learning Engineer, Data Mining

About the role

As a Senior Machine Learning Engineer on the Data Mining team, you will design and train large‑scale multimodal teacher‑student models, develop reinforcement‑learning pipelines, optimize real‑time inference deployment, improve reliability of the Omnitag data‑mining platform, and mentor junior engineers, directly accelerating the model improvement lifecycle for autonomous‑driving teams.

About the company

Motional is a driverless technology company, a joint venture of Hyundai Motor Group and Aptiv, focused on building safe, reliable and accessible autonomous vehicles. Headquartered in Boston with operations in the US and Asia, it emphasizes a progressive, diverse and inclusive culture while scaling its autonomous‑driving stack toward commercialization.

Requirements

• BS in Computer Science, Machine Learning, or related field, or equivalent experience.
• 6+ years of hands‑on machine learning engineering experience, focusing on model post‑training, optimization, and deployment.
• Strong experience with model distillation/teacher‑student training, including loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research, including policy optimization and reward design.
• Expert‑level proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX).
• Solid software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing inference.
• Demonstrated ability to ship production‑grade ML systems and mentor team members.
• MS or PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain‑of‑thought models, or LLM‑based planning.
• Background in autonomous driving, robotics, or real‑time decision‑making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops or contrastive learning for data mining.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open‑source contributions in RL, distillation, or efficient ML.