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.
Working at Swarmer means operating at the intersection of engineering rigor and frontline reality. The problems and environments are complex, and the stakes are very real. We built this software to enable democratic nations to defend themselves.
If you are motivated by building resilient systems that matter and by seeing the direct impact of your work, you’ll find purpose here.
Who we are looking for:
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.
What you’ll do: * 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. * Design, build, and operate data flows: ingestion from flights and sims, storage (data lakes / warehouses), labeling workflows, dataset versioning, and reproducible training/eval pipelines. * 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. * Improve model quality under field constraints: noisy sensors, limited compute, edge deployment (e.g. Jetson), and operational edge cases. * Partner closely with robotics, autonomy, and product engineers to ship changes that show up in real missions — not only offline metrics. * Build practical tooling and automation that speeds up the team: dataset curation, experiment tracking, evaluation harnesses, AI-assisted workflows. * Make pragmatic technical choices: when to retrain, when to fix data, when physics/heuristics beat another model.
You’ll be a good fit if you have: * 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.
What you’ll get: * 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.)
How’s the hiring process going:
✔️ Recruiter Screen → ✔️ Technical Interview → ✔️ Managerial Interview → ✔️ Final Interview with CEO →✔️ Background check → ✔️Offer
