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
Ready to Apply?
Skills Required
- Hands-on experience as a Data Scientist or ML Engineer working with computer vision, perception, or robotics-related machine learning
- Experience owning both machine learning model development and data infrastructure, including pipelines, lakes or warehouses, and dataset management
- Experience training, evaluating, and integrating computer vision or machine learning models into real systems
- Experience working with simulated data and hybrid real-world and simulation datasets, including measuring or reducing domain gaps
- Strong Python skills and experience with modern machine learning tooling such as PyTorch or TensorFlow
- Ability to write production-quality code rather than only prototypes or notebooks
- Strong fundamentals in mathematics and physics, including sensors, geometry, dynamics, and physical-world reasoning
- Upper-Intermediate English or higher
- Ability to work with high ownership in a fast-moving environment and ship, measure, and iterate
- Experience deploying or preparing models for edge or embedded platforms such as Jetson or Qualcomm
- Depth in ATR, object recognition, tracking, or autonomous systems
- Experience with robotics, drones, aerospace, defense, or OT environments
- Hardware-software integration experience
- Experience building or scaling data platforms for machine learning teams, including lakes, warehouses, feature or dataset stores, and labeling operations
- Experience closing the loop from field logs to datasets, models, and redeployed autonomy
- Daily power-user experience with AI engineering tools such as Cursor, Claude Code, or Codex
What We Do
Swarmer develops software systems that allow unmanned vehicles to act autonomously and work together in large teams. Its systems are centered on ethical AI and require humans to make all life-or-death decisions, while also allowing computers to do what they do best—processing large quantities of information and reacting to the rapidly changing environment in split seconds. Swarmer has successfully demonstrated swarms of up to 25 drones working together in GNSS-denied environments. In the near future, it plans to demonstrate combined-arms operations involving 100+ drones of various kinds, working in concert to seamlessly integrate UAS, USV, UGV, and stationary launchers into a single swarm that functions as one unit. Beyond defense applications, Swarmer’s hardware-agnostic platform positions the company to transform civilian markets from precision agriculture and emergency response to infrastructure inspections and environmental monitoring.
