Sensmore

4 months ago

ML Ops / Data Engineer - Robotics

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours 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

Website

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Sensmore

4 months ago

ML Ops / Data Engineer - Robotics

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

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

Industry: Industrial Automation

Website