Senior Machine Learning Engineer, Data Mining

About the Role

The role requires a deep understanding of machine learning fundamentals, including model distillation, reinforcement learning, and large-scale representation learning. You will be working on developing and deploying models that can operate in real-time, and your expertise in optimizing model performance and scalability will be essential.

The Data Mining team at Motional is responsible for developing and maintaining the Omnitag framework, which is used to power the discovery of critical intelligence hidden within petabytes of multimodal sensor data. As a senior engineer on this team, you will be expected to contribute to the development of this framework, as well as mentor junior engineers and collaborate with other teams to ensure the successful deployment of your models.

What You Will Do

• 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
• Build reinforcement learning-based policy learning and reasoning systems for autonomous driving applications
• Implement and scale reinforcement learning training workflows for simulation and real-world interaction
• Explore reward shaping, environment modeling, and multi-agent reinforcement learning where applicable
• Collaborate with backend engineers to deploy distilled and reinforcement learning 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
• Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior
• Integrate such systems into the broader autonomy stack as experimental or production components

What We Are Looking For

• 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 reinforcement learning-based reasoning
• Expert-level proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, or JAX)
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design
• Experience deploying machine learning models in cloud environments (AWS, GCP, or Azure) and optimizing for inference

Nice to Have

• Experience with Excel and data analysis
• Knowledge of large-scale representation learning and retrieval optimization
• Experience with reinforcement learning in autonomous driving applications
• Familiarity with agentic systems and autonomous reasoning

Benefits and Perks

• Competitive salary and benefits package
• Opportunity to work on cutting-edge autonomous driving technology
• Collaborative and dynamic work environment
• Professional development opportunities
• Flexible working hours and remote work options
• Access to the latest machine learning tools and technologies
• Health, dental, and vision insurance
• 401(k) matching and retirement planning
• Paid time off and holidays
• Employee assistance programs and mental health support

• Tip: Make sure to highlight your experience with model distillation and reinforcement learning in your application, as these are key requirements for the role.
• Tip: Familiarize yourself with the Omnitag framework and the Data Mining team's work at Motional to demonstrate your knowledge and interest in the company.
• Tip: Be prepared to provide examples of your experience with machine learning model deployment and optimization, as well as your expertise in Python and machine learning frameworks.
• Tip: Show a willingness to learn and adapt to new technologies and workflows, as the field of autonomous driving is constantly evolving.
• Tip: Showcase your ability to collaborate with cross-functional teams and communicate complex technical concepts to non-technical stakeholders.
• Tip: Consider including a portfolio of your work or examples of your projects to demonstrate your skills and experience in machine learning engineering.
• Tip: Be prepared to discuss your experience with cloud environments and deployment of machine learning models, as well as your knowledge of DevOps and continuous integration/continuous deployment (CI/CD) pipelines.