We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).
Requirements: 4+ years in ML engineering, applied CV, or a similar role combining model work with production data systems.
Hands-on experience with vision models - embeddings, VLMs, or object detection/segmentation.
Strong Python and comfort with the PyData stack (NumPy, PyArrow, Pandas, DuckDB).
Experience building data or ML pipelines that run at scale (not just notebooks).
Solid understanding of 3D geometry and camera models - or the mathematical background to ramp up quickly.
Good understanding of LLM agents and agentic workflows, with genuine interest in applying them to data and engineering problems.
Ability to work across team boundaries with algorithm and infrastructure people.
Strong advantage:
Experience with autonomous-driving datasets or perception pipelines.
Familiarity with dataset curation techniques (active learning, hard-example mining, distribution balancing).
Experience with GPU inference serving (vLLM, Triton, TensorRT).
Familiarity with vector databases or columnar analytics (LanceDB, DuckDB).
Experience with workflow orchestration (Argo, Airflow, Kubeflow).
This position is open to all candidates.