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Swarmer develops autonomous drone software enabling large coordinated teams to operate without pilots. The company became Ukraine's first defense startup to go public on NASDAQ in March 2026 following a $15M Series A, the largest investment in Ukrainian defense tech since the war began.
This is a hands-on, IC-first Senior Data Scientist role with real ownership across the full ML/CV lifecycle. You will own and contribute to existing ML/CV pipelines in the robotics autonomy stack (LMT, ATR, VISNAV), including training, evaluation, iteration, and integration with onboard/edge systems.
Key responsibilities include: designing and operating end-to-end data flows from flight and simulation ingestion through storage (data lakes/warehouses), labeling workflows, dataset versioning, and reproducible training/evaluation pipelines. You'll work extensively with simulated data—generating, validating, and mixing with real flight data while measuring and reducing sim-to-real gaps. You'll improve model quality under field constraints (noisy sensors, limited compute, edge deployment on platforms like Jetson) and partner closely with robotics, autonomy, and product engineers to ship changes that impact real missions, not just offline metrics.
You'll build practical tooling and automation (dataset curation, experiment tracking, evaluation harnesses, AI-assisted workflows) and make pragmatic technical choices about retraining, data fixes, and when physics/heuristics outperform models.
Required: strong hands-on experience as a Data Scientist/ML Engineer in computer vision, perception, or robotics ML. Proven ability to own both model work and data infrastructure (pipelines, lakes, dataset management). Experience training and evaluating CV/ML models (detection, tracking, recognition) and integrating them into real systems. Comfort with simulated and hybrid real+sim datasets; awareness of domain gap measurement and reduction. Solid Python and modern ML tooling (PyTorch/TensorFlow, experiment tracking, ETL basics); production-quality code ability. Fundamentals in math and physics (sensors, geometry, dynamics). Experience deploying or preparing models for edge/embedded platforms (Jetson, Qualcomm) is a strong plus. Real power-user experience with AI engineering tools (Cursor, Claude Code, Codex) and willingness to use daily. Upper-intermediate English or higher. High ownership in fast-moving environments.
Advantages: depth in ATR, object recognition, tracking, or autonomous systems; robotics, drones, aerospace, or defense/OT experience; hardware-software integration; building/scaling data platforms for ML teams; closing the loop from field logs to redeployed autonomy.
