ML Ops / Data Engineer – Robotics

About the role

As a Data Engineer at sensmore you will design, build, and maintain the data infrastructure that powers embodied AI and Vision‑Language‑Action Models for heavy‑duty robotics. You will work closely with robotics, machine‑learning and software engineers to ensure clean, reliable data flows from sensor arrays into training and inference pipelines.

Key responsibilities

• Build and operate data pipelines that ingest, process, and transform multi‑sensor telemetry (radar point‑clouds, video frames, log streams) into analytics‑ready and ML‑ready formats.
• Design scalable storage solutions such as high‑throughput data lakes and warehouses (e.g., S3, Delta Lake, Redshift, Snowflake).
• Enable ML Ops workflows by integrating tools like DVC or MLflow, automating model training/retraining triggers, and tracking data/model lineage.
• Ensure data quality through validation, monitoring, and alerting to detect anomalies and schema changes early.
• Collaborate cross‑functionally with embedded systems, robotics, and software teams to align on data schemas, APIs, and real‑time requirements.
• Optimize performance by tuning distributed processing, queries, and storage layouts for cost‑efficiency and throughput.
• Document and evangelize data schemas, pipeline architectures, and ML Ops practices for the whole team.

Required profile

• 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 such as DVC, MLflow, or Kubeflow.
• Experience designing and operating data warehouses or data lakes (Redshift, Snowflake, BigQuery, Delta Lake).
• Strong understanding of distributed systems, data serialization formats (Parquet, Avro), and batch vs. streaming paradigms.
• Excellent problem‑solving skills and ability to thrive 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 tools (Terraform, CloudFormation) and CI/CD for data workflows.
• Familiarity with Kubernetes and containerised deployments.
• Exposure to vision‑language or action‑planning machine‑learning models.

What we offer

• Opportunity to build physical AI for the world’s largest off‑highway machinery.
• Work with a pioneering robotics startup backed by Tier 1 investors.
• Combine cutting‑edge robotics research with real‑world heavy mobile equipment.
• Tailor your career path toward technical specialist or team lead.
• Great team culture with on‑site beverages and regular social events.
• Attractive compensation package and stock options.
• Assistance with relocation to Berlin.