Job Description

About Sitemark

Sitemark builds the platform that turns drone imagery of solar power plants into actionable insights for asset owners, O&M teams, and EPCs. We process huge volumes of aerial RGB and thermal imagery, detect what matters (anomalies, defects, construction progress), and deliver it in a product our customers actually use day-to-day.

We need someone who can help scale our AI capability so it reliably ships and moves real business metrics.

Tasks

The role

You'll own the AI/ML side of our platform: training and improving the computer-vision models that power our products, and making sure they actually ship and perform in production. Your work will 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 who delivers computer-vision solutions and knows how to navigate the landscape. 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.

Requirements

What you'll do

• Level up the MLOps backbone that lets us ship models reliably: experiment tracking, reproducible training, dataset versioning, model registry, deployment pipelines, monitoring in production, and a feedback loop from labeled operations data back into training. This is where AI work meets engineering, and it's a big part of what makes this role impactful.
• 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.
• Run the full experimental loop curate and improve datasets, design training runs, analyse errors, iterate.
• Tackle harder architectural problems when they matter — for example, models that need to reason over large spatial context (entire sites, not just tiles) where a standard fixed-resolution detector falls short.
• Integrate models into the product end-to-end. Your 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.
• Reason about business impact. Pick problems and approaches based on what actually moves the needle for our products and operations.

Benefits

Who we're looking for

Must-have

• Strong applied computer vision / deep learning experience. You've trained, fine-tuned, and debugged CV models — not just consumed APIs. You understand what's happening inside the models you use.
• Hands-on with the experimental loop dataset curation, augmentation, training, error analysis, iteration. You're comfortable when results are bad and know how to diagnose why.
• Pragmatic, product-oriented mindset. You can reason about how a model will be used in practice and what "good enough" looks like for the business. You prefer 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.
• Open to learning the integration side. You don't need to be a senior full-stack engineer on day one, but you should be motivated to grow into MLOps and integration work, and comfortable touching code beyond the model itself.
• High intelligence and learning velocity. We care more about how you think and how fast you grow than about years on a CV.
• Comfortable working in English in a small, fast-moving team.

Big plus

• Experience with aerial / drone / remote-sensing imagery (orthomosaics, geo-referencing, multi-band, large images).
• Non-visual imagery (thermal, multispectral) experience.
• Detection, segmentation, keypoint, or multi-scale architectures applied to large or high-resolution images.
• MLOps experience in production: experiment tracking, reproducible training, model registries, monitoring.
• Full-stack experience (Python, TypeScript, React, Postgres) — you'll get plenty of opportunities to use it.
• Weakly- or self-supervised learning, active learning loops.

How you'll work

• You report to the Head of Product & Engineering Coaching and technical sparring with the Engineering Lead
• You'll work in cross-functional squads with platform engineers and our product team.
• You'll partner closely with the operational teams and our customers. Tight feedback loop.
• We value shipping over perfection, and getting the architecture right when it matters.

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.