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Senior Machine Learning Data Engineer
·Israel
On-siteFull-timeData EngineeringAutonomous VehiclesAutomotiveSoftware Development
Join a small, independent AI engineering team at a company developing advanced driver-assistance and autonomous driving technologies. You will focus on the machine learning and data curation side of a unified autonomous vehicle dataset pipeline, working with vision models, GPU inference, and large-scale sensor data to produce high-quality training sets.
Responsibilities
- Build and improve the data curation pipeline, including vision-model embeddings, scene detection, VLM-based analysis, scoring, deduplication, and sampling for balanced datasets.
- Run and optimize GPU inference at scale across thousands of driving sessions using workflow orchestration tools.
- Develop scoring and sampling strategies to ensure rare scenarios (night driving, adverse weather, hazardous situations) are well-represented.
- Collaborate with algorithm teams to identify data gaps affecting model performance and translate them into curation criteria.
- Build validation and diagnostics to measure dataset quality for training effectiveness.
- Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).
Requirements
- 4+ years in data engineering or backend/software engineering with production data pipelines.
- Strong Python and PyData stack (NumPy, PyArrow, Pandas, DuckDB).
- Background in research, algorithms, or ML sufficient to read papers and discuss model outputs.
- Comfort working with vision-model outputs as data (embeddings, detection results, VLM responses).
- Ability to collaborate across algorithm, infrastructure, and internal teams.
Nice to have
- Experience with autonomous-driving datasets or perception pipelines.
- 3D geometry and camera model intuition or mathematical background.
- Workflow orchestration experience (Argo, Airflow, Kubeflow).
- Familiarity with vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale).
- Understanding of curation concepts (active learning, hard-example mining, distribution balancing).
- Exposure to LLM agents or agentic workflows for data tasks.