AI Engineer

As an AI/ML Engineer at Sitemark, you will own the AI/ML side of the platform, training and improving computer-vision models that turn drone and robot imagery from renewable energy sites into actionable answers. You'll run the full experimental loop—curating datasets, designing training runs, analyzing errors, and iterating—while also building the MLOps backbone to ship models reliably in production. You'll integrate models end-to-end into the product, ensuring they perform on real data and move business metrics, and you'll work in cross-functional squads alongside platform engineers, product, and operations teams.

Responsibilities

• 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. This is where AI meets engineering, and it's a big part of what makes the role impactful.
• 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

• 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.

Nice to have

• 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

• 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.