ML Ops / Data Engineer - Robotics

sensmore is a Berlin/Potsdam-based robotics startup delivering production-proven automation for industries where the world’s raw materials are extracted, moved, and processed. Its automation system transforms heavy machines into intelligent, automated robots powered by Physical AI and vertically integrates them into the full production environment: from the machine and safety infrastructure to network infrastructure, site processes, and operational interfaces.

Co-developed with customers, sensmore is backed by Point Nine Capital, leading industry investors, the State of Brandenburg, and the European Union.

As our Data Engineer, you will design, build, and maintain the data infrastructure that powers Sensmore’s embodied AI and Vision-Language-Action Models (VLAMs). You’ll collaborate with Robotics, ML and Software engineers to ensure clean, reliable data flows from our sensor arrays (radar, LiDAR, cameras, IMUs) into training and inference pipelines. This role blends classic data engineering (ETL/ELT, warehouse design, monitoring) with ML Ops best practices: model versioning, data drift detection, and automated retraining.

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  Build & operate data pipelines: Ingest, process, and transform multi-sensor telemetry (radar point-clouds, video frames, log streams) into analytics-ready and ML-ready formats.

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  Design scalable storage: Architect high-throughput, low-latency data lakes and warehouses (e.g., S3, Delta Lake, Redshift/Snowflake).

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  Enable ML Ops workflows: Integrate DVC or MLflow, automate model training/retraining triggers, track data/model lineage.

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  Ensure data quality: Implement validation, monitoring, and alerting to catch anomalies and schema changes early.

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  Collaborate cross-functionally: Partner with Embedded Systems, Robotics, and Software teams to align on data schemas, APIs, and real-time requirements.

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  Optimize performance: Tune distributed processing, queries, and storage layouts for cost-efficiency and throughput.

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  Document & evangelize: Maintain clear documentation for data schemas, pipeline architectures, and ML Ops practices to uplift the whole team.

• 3+ years of hands-on experience building production data pipelines in the cloud (AWS, GCP, or Azure).

• Proficiency in Python, SQL, and at least one big-data framework.

• Familiarity with ML Ops tooling: DVC, MLflow, Kubeflow, or similar.

• Experience designing and operating data warehouses/data lakes (e.g., Redshift, Snowflake, BigQuery, Delta Lake).

• Strong understanding of distributed systems, data serialization (Parquet, Avro), and batch vs. streaming paradigms.

• Excellent problem-solving skills and the ability to work in ambiguous, fast-paced environments.

• Background in robotics or sensor data (radar, LiDAR, camera pipelines).

• Knowledge of real-time data processing and edge-computing constraints.

• Experience with infrastructure as code (Terraform, CloudFormation) and CI/CD for data workflows.

• Familiarity with Kubernetes and containerized deployments.

• Exposure to vision-language or action-planning ML models.

Heavy machinery, light years ahead.

sensmore automates the world's largest machines with unprecedented intelligence. Our proprietary Physical AI enables heavy machines such as wheel loaders to instantly adapt to dynamic environments and execute new tasks without prior training.

We integrate cutting-edge robotics into a platform powering intelligence and automation products - transforming productivity and safety for customers in mining, construction, and adjacent industries today.

We are proudly backed by Point Nine and other Tier 1 investors.