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 Discovery: 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.

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.

Benefits

Health insurance
Dental insurance
Vision insurance
401k with a company match
Health savings accounts
Life insurance
Pet insurance
Bonuses

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning engineeringmodel distillationreinforcement learningpolicy optimizationloss functionstraining strategiesPythonPyTorchTensorFlowJAX

Soft Skills

mentoringcollaborationproblem-solvingcommunicationleadership