Senior Machine Learning Engineer, Data Mining

Motional; Pittsburgh (Allegheny County); On-site; USD 110,000 - 150,000; Full time

Job summary

Motional, located in Pittsburgh, is seeking a Senior Machine Learning Engineer for its Data Mining team. You will architect teacher-student models for multimodal sensor data and contribute to the development of reinforcement learning systems aimed at enhancing data discovery for autonomous vehicles.

This pivotal role will involve collaboration with various engineering teams to optimize model deployment and ensure the reliability of our ML operations, driving significant advancements in autonomous driving technology.

Qualifications

• Experienced in large-scale representation learning and model distillation.
• Proven track record in deploying ML models in production.
• Skilled in reinforcement learning techniques and workflows.

Responsibilities

• Design and implement teacher-student model frameworks for multimodal sensor data.
• Develop RL-based systems for data discovery in autonomous driving.
• Collaborate with engineers to optimize model deployment in real-time.

Skills

Architecting and Training Distilled Models

Reinforcement Learning (PPO, DQN)

Model Deployment Optimization

Mentorship and Collaboration

Education

Master's or PhD in Computer Science, ML, or related field

Tools

Python

TensorFlow or PyTorch

Docker

Job description

Mission Summary

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.

Senior Machine Learning Engineer: Data Mining Team

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.

What You’ll Do

• 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.