ML Ops / Data Engineer - Robotics

Sensmore
Berlin, Potsdam
Workplace: OnsiteFull timeFunction: Software EngineeringExperience: 3+ yearsSkills: ["Problem-solving","Collaboration","Communication"]

Data Engineer in a robotics context responsible for designing, building, and maintaining data infrastructure powering embodied AI and Vision-Language-Action Models. Collaborates with Robotics, ML and Software teams to ensure clean data flows from multi-sensor arrays (radar, LiDAR, cameras, IMUs) into training and inference pipelines, while applying ML Ops best practices for model/version control, data drift detection, and automated retraining.

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FursaFursa
Sensmore
Sensmore
6 months ago

ML Ops / Data Engineer - Robotics

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Last checked: 1 hour agoStatus: Live

Job Summary

Data Engineer in a robotics context responsible for designing, building, and maintaining data infrastructure powering embodied AI and Vision-Language-Action Models. Collaborates with Robotics, ML and Software teams to ensure clean data flows from multi-sensor arrays (radar, LiDAR, cameras, IMUs) into training and inference pipelines, while applying ML Ops best practices for model/version control, data drift detection, and automated retraining.
Location: Berlin, Potsdam
Workplace: Onsite
Employment Type: Full time
Job Function: Software Engineering

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.

Pay and Benefits

Equity and Bonus:Equity
Perks:EquityRelocation

Key Requirements

  • •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.
Experience:3+ yearsRoboticsData engineeringMl ops
Skills:Problem-solvingCollaborationCommunication
Tech Stack:PythonSQLDVCMLflowKubeflowDelta LakeRedshiftSnowflakeBigQueryParquetAvroAWSGCPAzureTerraformCloudFormationKubernetes

Company Brief

Sensmore
Develops AI-driven acoustic sensing and anomaly-detection solutions for industrial equipment. Uses edge audio sensors and machine learning to monitor machinery, detect faults early, and enable predictive maintenance to reduce downtime and maintenance costs.
Industry: Industrial Automation
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