Tech Stack / Keywords
Podsumowanie oferty
Own part of a largely greenfield data platform for a physical AI program building autonomous construction machines. Turn multimodal sensor data into trusted, versioned datasets for vision-language-action model training and simulation, using Python, Spark or Databricks.
Wymagania
- Proven commercial experience in data architecture or data engineering on platforms at scale, with hands-on delivery.
- Experience with images, video or point clouds on object storage, backed by metadata catalogs and registries.
- Practical experience with curation, dataset splits, holdouts and reproducible dataset versions.
- Hands-on lineage and versioning from raw data to training datasets using LakeFS, DVC, Delta or equivalent.
- Strong knowledge of Parquet, Iceberg or Delta, plus batch and streaming ingestion with Kafka or Kinesis.
- Hands-on work with at least one major cloud (AWS S3, Azure or GCP), Spark or Databricks, and Python.
- Comfort using AI coding tools to turn architecture into working software quickly.
Nice to have:
- Experience with autonomous vehicle, robotics or ADAS data (ROS bags or MCAP, sensor logs, drive or scene data).
- Experience building annotation and labeling pipelines, including auto-labeling.
- Work with synthetic or simulated imagery and sensor data for training or testing.
- Hands-on use of MLflow or similar tools for experiment and artifact tracking.
Obowiązki
- Curation & Annotation Flow: Design the end-to-end flow from landed sensor data to training-set creation for a data-collection fleet scaling to 500+ machines.
- Metadata Registry & Cataloging: Build the metadata registry for every asset and define the cataloging approach used by ML and robotics engineers.
- Lineage, Versioning & Lifecycle: Set up lineage, versioning, lifecycle and retention from raw sensor logs to each training dataset, so every model can be traced back to its source data.
- Simulation-Ready & Synthetic Data: Define how real data becomes simulation-ready and how synthetic data is stored, versioned and mixed with real data.
- Data Contracts & Implementable Architecture: Agree data contracts with the perception and kit teams, and produce architecture artifacts engineers can start building on right away, working hands-on with AI coding agents.
Benefity
- Flexible schedule.
- Benefits package including medical insurance and sports.
- Corporate social events.
- Professional development opportunities.
- Well-equipped office.
Elastyczne godziny
Opieka zdrowotna
Karta sportowa
Spotkania integracyjne
Inne informacje
- Willingness to travel to client sites when needed.
Grid Dynamics
17 aktywnych ofert