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Senior Computer Vision Data Engineer

Mobileye·ישראל·
לא צויןמשרה מלאהData EngineeringAutomotive

Work on a small, independent team building a unified AV dataset used to train and evaluate next-generation perception models. Focus on the curation and ML side of the data pipeline, including vision-model embeddings, scene detection, VLM-based analysis, scoring, deduplication, and sampling. Run and optimize GPU inference at scale, develop scoring strategies, and build validation tools to ensure dataset quality.

תחומי אחריות

  • Build and improve the data curation pipeline, including vision-model embeddings, scene detection, VLM-based scene analysis, scoring, deduplication, and sampling.
  • Run and optimize GPU inference at scale across thousands of driving sessions using workflow orchestration.
  • Develop scoring and sampling strategies to ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented.
  • Collaborate with algorithm teams to identify data gaps and translate them into curation criteria.
  • Build validation and diagnostics to measure dataset quality, not just pipeline health.
  • Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

דרישות

  • 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 understand model outputs and collaborate with algorithm engineers.
  • Comfort working with vision-model outputs as data (embeddings, detection results, VLM responses).
  • Ability to work across team boundaries.

יתרון

  • Experience with autonomous-driving datasets or perception pipelines.
  • 3D geometry and camera model intuition.
  • Workflow orchestration (Argo, Airflow, Kubeflow).
  • Vector databases or columnar analytics (LanceDB, DuckDB, Parquet at scale).
  • Familiarity with curation concepts (active learning, hard-example mining, distribution balancing).
  • Exposure to LLM agents or agentic workflows.

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