Senior Machine Learning Engineer, Data Mining

Motional•Boston, MA

10h•Hybrid

About The Position

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag , our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

Requirements

BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
Demonstrated ability to ship production-grade ML systems and mentor team members.
Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Nice To Haves

MS/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, using the model to find the data that breaks the model.
Experience with ML-based data mining, active learning, or contrastive learning.
Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
Publications or open-source contributions in RL, distillation, or efficient ML.

Responsibilities

Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
Reinforcement Learning for Data Discover : Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
Drive Production Reliability : Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
Mentor and Collaborate : Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.