Senior Data Scientist

Swarmer develops software that makes drones autonomous and allows them to operate together, in large, coordinated teams — no pilots needed. Our technology has been battle-tested in Ukraine— the world’s most intense proving ground for drone warfare.

In March 2026, we became the first Ukrainian defense startup to go public on NASDAQ, following a $15M Series A — the largest investment in a Ukrainian defense tech company since the start of the war.

We are looking for a hands-on Data Scientist who can own and contribute to existing ML/CV pipelines in a robotics autonomy stack, and who can also own and grow the data side of the house — from collection and labeling through lakes/warehouses to training and evaluation. This is an IC-first role with real ownership: you write code, ship models and data products, and improve the loops that turn flight and simulation data into better autonomy.

We are not looking for a pure researcher, a detached platform architect, or someone who only does notebooks. We need someone who can work end-to-end across models, data, and robotics constraints, with strong judgment and delivery discipline.

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  Own and contribute to existing ML/CV flows in our robotics autonomy and perception stack (LMT, ATR, VISNAV and related systems): training, evaluation, iteration, and integration with onboard/edge pipelines.

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  Design, build, and operate data flows: ingestion from flights and sims, storage (data lakes / warehouses), labeling workflows, dataset versioning, and reproducible training/eval pipelines.

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  Work extensively with simulated data: generate, validate, mix with real flight data, measure sim-to-real gaps, and improve dataset quality for perception and autonomy models.

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  Improve model quality under field constraints: noisy sensors, limited compute, edge deployment (e.g. Jetson), and operational edge cases.

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  Partner closely with robotics, autonomy, and product engineers to ship changes that show up in real missions — not only offline metrics.

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  Build practical tooling and automation that speeds up the team: dataset curation, experiment tracking, evaluation harnesses, AI-assisted workflows.

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  Make pragmatic technical choices: when to retrain, when to fix data, when physics/heuristics beat another model.

• Strong hands-on experience as a Data Scientist / ML Engineer working on computer vision, perception, or robotics-related ML.

• Proven ability to own both model work and data infrastructure (pipelines, lakes/warehouses, dataset management) — not only one side.

• Experience training and evaluating CV/ML models (detection, tracking, recognition, or similar) and integrating them into real systems.

• Comfort working with simulated data and hybrid real+sim datasets; awareness of domain gap and how to measure/reduce it.

• Solid Python and modern ML tooling (PyTorch/TF, experiment tracking, data/ETL basics); ability to write production-quality code, not only prototypes.

• Fundamentals in math and physics — comfortable reasoning about sensors, geometry, dynamics, and the physical world before defaulting to “let’s train a model.”

• Experience deploying or preparing models for edge / embedded platforms (e.g. Jetson, Qualcomm) is a strong plus; cloud-only experience is not enough by itself.

• Real power-user experience with AI engineering tools (Cursor, Claude Code, Codex, or similar) and willingness to use them daily.

• Upper-Intermediate English or higher.

• Ability to operate with high ownership in a fast-moving environment: ship, measure, iterate.

Would be an advantage:

• Depth in ATR, object recognition, tracking, or autonomous systems.

• Experience with robotics, drones, aerospace, or defense / OT environments.

• Hardware–software integration experience.

• Building or scaling data platforms for ML teams (lakes, warehouses, feature/dataset stores, labeling ops).

• Experience closing the loop from field logs → datasets → models → redeployed autonomy.

• A chance to shape core autonomy and perception systems at the heart of Ukraine’s defense tech ecosystem and its international expansion

• Direct impact in a high-stakes industry where security decisions carry real-world consequences

• Professional growth through cutting-edge defense technologies and exposure to international security best practices

• Competitive salary, benefits package (insurance, paid sick leaves, 20 paid days off per year)

• Benefits of the defense sector (reservation, etc.)

✔️ Recruiter Screen → ✔️ Technical Interview → ✔️ Managerial Interview → ✔️ Final Interview with CEO →✔️ Background check → ✔️Offer

Ready to Apply?