Senior Machine Learning Engineer, Data Mining

Role Description

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of our ML-powered multimodal data mining framework, Omnitag. You will:

• 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.

Qualifications

• 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.

Requirements

• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers.

Benefits

• Medical, dental, vision.
• 401k with a company match.
• Health saving accounts.
• Life insurance.
• Pet insurance.
• And more.

Salary Range

$172,000 — $229,000 USD