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Tech Stack
Tools & technologiesPostgresPythonReactTypeScript
About the role
Key responsibilities & impact- Train, fine-tune, and ship computer-vision models for tasks like thermal anomaly detection and classification, defect detection on high-resolution imagery, object detection on drone imagery, and stitching/co-registration support.
- Level up the MLOps backbone that lets us ship reliably: experiment tracking, reproducible training, dataset versioning, a model registry, deployment pipelines, production monitoring, and a feedback loop from labeled operations data back into training.
- Run the full experimental loop end to end: curate and improve datasets, design training runs, analyse errors, and iterate.
- Take on the harder architectural problems when they matter — for example, models that reason over large spatial context (an entire site, not just a tile) where a standard fixed-resolution detector falls short.
- Integrate models into the product end to end. A model isn't done when the metric looks good — it's done when it's running on real data in the platform and making the team or the customer faster.
- Choose problems and approaches based on business impact — what actually moves the needle for our products and operations.
Requirements
What you’ll need- Strong applied computer vision / deep learning experience — you've trained, fine-tuned, and debugged CV models, not just called APIs, and you understand what's happening inside them.
- Hands-on with the experimental loop: dataset curation, augmentation, training, error analysis, iteration. When results are bad, you know how to diagnose why.
- A pragmatic, product-oriented mindset — you reason about how a model will actually be used, what "good enough" means for the business, and the shortest path to a real result.
- Strong fundamentals and clean engineering instincts. You write code meant to live in production — readable, testable, maintainable — not just notebook scratch.
- Motivated to grow into the integration and MLOps side, and comfortable touching code beyond the model itself. (You don't need to be a senior full-stack engineer on day one.)
- High intelligence and learning velocity — we care more about how you think and how fast you grow than years on a CV.
- Comfortable working in English in a small, fast-moving team.
- Big plus: Aerial / drone / remote-sensing imagery (orthomosaics, geo-referencing, multi-band, large images).
- Non-visual imagery (thermal, multispectral).
- Detection, segmentation, keypoint, or multi-scale architectures applied to large or high-resolution images.
- Production MLOps: experiment tracking, reproducible training, model registries, monitoring.
- Full-stack experience (Python, TypeScript, React, Postgres) — you'll get plenty of chances to use it.
- Weakly- or self-supervised learning, active-learning loops.
Benefits
Comp & perks- Real impact, fast: a clearly identified gap, a concrete roadmap, and customers waiting on the results. Your models will ship.
- Breadth: from datasets and model work through MLOps and into product integration — you'll grow across the stack as much as you want.
- A strategic seat: AI is central to where Sitemark is going, and you'll help shape that direction, not just execute on it.
- A pragmatic culture: we care about results, not theatre — the boring solution when it works, the hard one when it doesn't.
- Work on a mission that matters: accelerating the world's transition to renewable energy.
- Competitive compensation including meaningful equity (stock options) with real upside.
- Remote-friendly within Central European time zones — we have team members across Belgium and Poland, and we're open to additional locations with enough overlap with CET hours.
ATS Keywords
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Hard Skills & Tools
computer visiondeep learningdataset curationdata augmentationerror analysisMLOpsexperiment trackingmodel registriesPythonTypeScript
Soft Skills
pragmatic mindsetclean engineering instinctshigh intelligencelearning velocityteam collaboration