Senior ML Data Engineer (Computer Vision)

Location: Jerusalem, Israel; hybrid workplace

Company

hirify.global's R&D Software organization builds infrastructure and solutions for developing advanced autonomous-vehicle perception algorithms.

What you will do

• Build and improve ML-driven data curation pipelines using vision-model embeddings, scene detection, VLM analysis, scoring, deduplication, and sampling.
• Run and optimize GPU inference at scale across thousands of driving sessions using workflow orchestration.
• Develop strategies that preserve rare and important scenarios, including night driving, adverse weather, and hazardous situations.
• Work with algorithm teams to translate model-performance data gaps into curation criteria.
• Build dataset-quality validation and diagnostics for training readiness.
• Contribute to the dataset SDK, converters, and 3D geometry tooling, including camera projection, calibration, and coordinate transforms.

Requirements

• 4+ years of data engineering or backend/software engineering experience with production data pipelines.
• Strong Python and PyData experience, including NumPy, PyArrow, Pandas, and DuckDB.
• Background in research, algorithms, or ML, with the ability to understand papers and model outputs.
• Experience working with vision-model outputs such as embeddings, detection results, and VLM responses.
• Ability to collaborate across algorithm, infrastructure, and data engineering teams.

Nice to have

• Experience with autonomous-driving datasets or perception pipelines.
• 3D geometry and camera-model knowledge.
• Workflow orchestration experience with Argo, Airflow, or Kubeflow.
• Experience with vector databases or columnar analytics, including LanceDB, DuckDB, or Parquet at scale.
• Knowledge of active learning, hard-example mining, distribution balancing, or agentic workflows for data tasks.

Culture & Benefits

• Work in a small, independent team of experienced engineers.
• Use a DevOps-style approach with shared ownership and cross-team collaboration.
• Contribute across algorithms, software, infrastructure, and the broader data pipeline.
• AI tools may support parts of the hiring process, while final hiring decisions remain human-led.