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.

Role Overview

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.

Key Responsibilities

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.

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

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

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

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

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

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

Required Qualifications

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.

Preferred Skills

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.

What We Offer

Build physical AI for the world's largest off-highway machinery – making them intelligent, safe, and ready for every tough task

Join the pioneer in intelligent robotics backed by Point Nine & other Tier 1 investors

Combine cutting-edge robotics research in end-to-end learning & Vision Language Action Model with real-world heavy mobile equipment

Tailor your own career path, whether you like to become technical specialist or technical team lead

Experience a great team culture, beverages, and an amazing office environment

Benefits

Attractive compensation package and stock options.

Beverages on-site and regular social events.

Engage with top-tier researchers, engineers, and thought leaders.

Influence the future of robotic technologies and tackle significant technological challenges.

Assistance with relocation to Berlin.

About Us

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.