AI/ML Engineer

AI/ML Engineer

At Sitemark, AI sits at the heart of the product. Drones and robots capture vast amounts of imagery from renewable energy sites, and our computer-vision models turn it into the answers customers act on. We need someone to scale that capability so it ships reliably and moves real business metrics.

As our AI/ML Engineer, you own the AI/ML side of the platform: training and improving the computer-vision models behind our products, and making sure they actually ship and perform in production. You’ll raise our throughput across model implementation, training runs, and dataset iteration — directly unblocking the team and our customers.

We’re looking for a pragmatic engineer-scientist, not a paper-chaser. Models exist to solve real problems: if an off-the-shelf model fine-tuned on our data does the job, that’s a great answer. We care about results in the product, not novelty in a paper. No solar or energy background required — we’ll teach you the domain; curiosity matters more.

You’ll report to the Head of Product & Engineering, with coaching and technical sparring from the Engineering Lead, and work in cross-functional squads alongside platform engineers, the product team, and — closely — our operations teams and customers.

What You’ll Do

• 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.

What Success Looks Like After 6–12 Months:

• Your models are running in production on real data, moving the metrics our customers and operations teams care about.
• The MLOps backbone is solid — training is reproducible, experiments are tracked, and shipping a new model is routine, not a fire drill.
• A feedback loop runs from labeled operations data back into training, so the models keep getting better on their own cadence.
• Throughput across model work, training runs, and dataset iteration is meaningfully higher than when you started.
• AI is unblocking the roadmap, not bottlenecking it — the gaps we hired you to close are closed.

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.

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

• 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.

Why This Role Is Interesting

• Real impact, fast. We have a clearly identified gap, a concrete roadmap, and customers waiting on the results. Your models will ship.
• Breadth. From dataset and model work, through MLOps, into product integration. You’ll grow across the stack as much as you want to.
• Strategic seat. AI is central to where Sitemark is going. You’ll help shape that direction, not just execute on it.
• Pragmatic culture. We care about results, not theatre. We pick the boring solution when it works and invest in the hard one when it doesn’t.

Location

Remote-friendly, within compatible time zones. We have team members across Belgium and Poland and are open to additional locations with sufficient overlap with Central European working hours.