ML Engineer
Core
Building the AI layer's learning loop for a robotics data platform, focusing on evaluations, retrieval, and cost optimization for fleet data.
Role type
Applied ML Engineer (LLM systems & robotics data)
Builds
Evaluation systems, retrieval pipelines, task-specific models, and inference optimization for robot fleet data
Domain
Robotics, AI/ML, Data Infrastructure
Deliverable
production ML models
Required skills
Applied ML, LLM systems (evals, retrieval, fine-tuning), Python for production, measurement-driven development
Preferred skills
Custom model training (embeddings, fine-tunes), time-series/sensor data experience, inference cost optimization at scale, open-source contributions
Technologies
Python, LLM frameworks, fleet data stacks
Responsibilities
Build evaluation systems for quality, speed, and cost; optimize retrieval over messy fleet data; drive inference cost down via routing, caching, and distillation; train task-specific models where APIs fall short; convert cross-fleet usage into improving datasets and detectors