ML Data Engineer (m/f/d) - Sensor Data & Pipelines
autonomous-teaming
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This role on the market
16 open pipelines roles across 12 companies are on ApplySarthi right now, most of them in Pune (2), Hyderabad (1), Bengaluru (1).
- Data Engineer – Data PipelinesBDIPlus · bengaluru
- Sr. Staff Integration Engineer (Security Platform and Data Pipelines)Zscaler
- Senior Software Engineer (C++) - Sensor PipelinesLatitude AI
- Associate Data Scientist – Modeling, Analytics & PipelinesAeratechnology · pune
- Staff Software Engineer, Lakeflow Pipelines DRDatabricks
What pipelines roles keep asking for: Python (44%), System design (44%), AWS (31%), Databricks (31%), ETL (31%), Observability (31%), SQL (31%), C++ (25%) — counted across their open postings here.
AWS jobs · Computer vision jobs · Docker jobs · ETL jobs
autonomous-teaming has 2 open roles listed here.
Counted across 14 company job boards, updated as roles open and close.
Preparing for this interview
Interviews for pipelines roles keep coming back to Python, System design, AWS, Databricks. Practise those questions before you sit with autonomous-teaming.
Questions you are likely to be asked
- Why do you want to join autonomous-teaming?
- What is your experience with ETL? Tell me one thing you learned the hard way.
- When would you not use machine learning for a problem?
- Walk me through a model you built, from the data to how it was used.
- How did you know your model was actually good, and not just good on your test set?
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Practise the ML Data Engineer (m/f/d) - Sensor Data & Pipelines at autonomous-teaming interview free →What we offer Work in an international, agile team creating the future of autonomous systems Grow your career in a expanding and ambitious engineering team Build innovative products using state-of-the-art technologies in AI, robotics, and autonomy Benefit from a steep learning curve and continuous development Enjoy team events and a strong, collaborative culture Your mission This role owns the data foundation of our perception systems end-to-end — the layer that directly determines model performance in real-world environments. You'll set the technical direction for how we collect, curate, and continuously improve the datasets behind object detection, working as a senior technical partner to ML, perception, and robotics teams — turning raw, messy sensor data into reliable, production-grade systems at scale. You will take full ownership of the ML data lifecycle — from architecture decisions on ingestion and pipelines, through labeling strategy and QA, to driving continuous, metrics-informed dataset improvement — and will be expected to bring judgment and prior experience to how this is done, not just execute a defined process. What you'll do: Architect and own scalable pipelines for ingesting, organizing, and preprocessing large volumes of time-series camera and multi-sensor data (RGB, IR, thermal, depth, IMU) Drive the strategy behind our object detection datasets, ensuring quality, diversity, and statistical representativeness at scale Design and operate active learning loops that connect model performance directly to data selection and improvement priorities Own labeling workflows end-to-end — tooling decisions, QA methodology, consistency standards, and coordination of annotation efforts Partner closely with AI Engineers to diagnose model weaknesses, bias, and drift, and translate findings into concrete dataset strategy Plan and lead data collection campaigns (field recordings, drone/video capture) to close gaps with high-value real-world data Build internal tools and dashboards that give the org visibility into dataset quality, distribution, and performance gaps Your profile 5+ years of hands-on experience in Python and data processing frameworks (Pandas, NumPy, vectorized operations, multiprocessing) Proven track record building and owning ETL/ELT pipelines for large-scale video and sensor datasets in production Deep experience with data orchestration and lifecycle management for ML/computer vision workflows, including dataset versioning and reproducibility Strong command of object detection pipelines (Detectron2, MMDetection, COCO format, bounding-box standards) Demonstrated experience designing active learning, uncertainty sampling, or semi-supervised dataset workflows Deep familiarity with data annotation platforms (CVAT, Label Studio) and building automated QA/consistency checks Strong grasp of evaluation metrics for object detection (IoU, mAP, precision-recall curves, class-wise metrics) Comfortable owning decisions around databases (SQL/NoSQL), file systems, and large-scale image, video, and sensor dataset management Track record of working cross-functionally and influencing perception, deployment, robotics, and data infrastructure teams Fluent in English; German and/or French are a plus Nice to have Experience with cloud storage and MLOps tools (AWS S3, MinIO, ClearML, MLFlow, Weights & Biases). Familiarity with ROS / robotics data formats (bag files, TF trees, sensor_msgs), Docker, or embedded ML workflows. Prior work with robotics, drones, or multi-sensor perception systems, including IR, LiDAR, radar, or audio datasets. What else Outside-the-box creativity with a blend of conceptual and systematic design thinking. High intrinsic motivation, attention to detail, and strong problem-solving mindset. Structured, methodical, and reliable execution, even under uncertainty. Humble, collaborative, and mission-driven — values collective success over ego. High ethical standards and disciplined work ethic. Extra-curricular achievements, leadership, or unique projects are a plus. NATO-aligned nationality or close ally citizenship is required. Why us? Join us to shape the future of AI-driven defense! Find more English Speaking Jobs in Germany on Arbeitnow
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Listed on arbeitnow · posted 2026-09-29. ApplySarthi collects openings and links to application pages; the role is advertised by autonomous-teaming, not by us.