Robotics Data Engineer

About the role

We are looking for a Robotics Data Engineer to design, build, and operate the data infrastructure that transforms raw robot experience into reliable training and evaluation datasets. You will collaborate closely with model and robotics engineers, ensuring that data pipelines are robust, scalable, and easy to use.

Key responsibilities

• Build pipelines for ingesting multimodal robot data (video, images, states, actions, force, telemetry, logs).
• Implement timestamp alignment, calibration metadata handling, validation, transformation, and dataset packaging.
• Develop tools for dataset discovery, filtering, versioning, sampling, annotation, and export.
• Create automated data‑quality checks and dashboards for completeness, consistency, corruption, drift, and duplication.
• Design efficient storage layouts and lifecycle policies for large robotics datasets.
• Maintain lineage and reproducibility from source recording to model‑ready dataset.
• Integrate data systems with collection rigs, labeling workflows, model training, and evaluation infrastructure.
• Improve throughput and cost through profiling, batching, parallelism, and optimal compute/storage choices.
• Investigate pipeline failures and data anomalies, driving permanent fixes.
• Document schemas, operating procedures, and data contracts for internal users.

Required profile

• Bachelor’s degree in computer science, data engineering, robotics, or equivalent experience.
• At least 5 years of professional experience with Python and SQL, following solid software‑engineering practices.
• Proven experience building production data pipelines and handling large datasets.
• Strong collaboration and documentation habits.

Required skills

• Python
• SQL
• Linux, containers, version control
• Cloud or on‑prem compute environments
• File/object storage, databases, schemas, partitioning, data‑lifecycle concepts
• Workflow orchestration, observability, validation, failure recovery
• Multimodal data handling, timestamps, metadata, reproducibility
• Knowledge of ROS bags, video pipelines, Spark, Ray, Kubernetes, Airflow, Dagster (nice to have)
• Experience with annotation systems, dataset versioning, ML data platforms (nice to have)
• Understanding of privacy, access control, data‑governance practices (nice to have)