Machine Learning Engineer

The role

We record first-person footage of skilled physical work with our own multi-camera capture rigs, and we fine-tune embodied models on what we capture. You own everything between the two: the pipeline that turns raw multi-sensor recordings into training-grade datasets, and the fine-tuning runs that prove what the data is worth.

A training run is only as good as its worst batch. Your quality gates decide what gets in.

What you'll do

• Build the pipeline from raw capture sessions to training-ready datasets: ingest, sync validation, deduplication, QA gates.
• Fine-tune embodied and vision models on our captures, and iterate from failure analysis.
• Define data-quality metrics and validation gates. Root-cause regressions across the pipeline.
• Build auto-labeling and human-in-the-loop annotation tooling that scales past manual review.
• Work with multi-sensor streams: multi-camera video, IMU, timestamps that have to agree.
• Translate model failures into concrete changes to what and how we capture, working with hardware and field operations.
• Run and track training jobs: evals, dashboards, regressions caught before they cost a week.

What we look for

• Hands-on experience training and fine-tuning vision or embodied models, shipped into production.
• You've built large-scale data pipelines and have a quantitative mindset about data quality.
• Strong Python and PyTorch. You own problems from raw bytes to training curves without waiting for permission.
• You debug datasets with the same rigor as models: most failures live in the data.

Nice to have

• Imitation learning or VLA fine-tuning: ACT, diffusion policies, or similar.
• Egocentric video understanding and datasets like Ego4D or Epic-Kitchens.
• Multi-view geometry, SLAM, or hand-pose estimation.
• GPU cluster training infrastructure, and model optimization for deployment.

Apply

Email team@mimeticmachines.com with the subject line "Application: Machine Learning Engineer". Attach a resume or point us at work you're proud of, and tell us what you'd do in your first month. Solved the puzzle? Say so.

We read every application. We write back within a week.