AIML Engineer

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.

The role

Youll 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.

Were 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 thats a great answer. We care about results in the product not novelty in a paper.

No solar or energy background required well teach you the domain; curiosity matters more.

What youll 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 its 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 isnt done when the metric looks good its done when its 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.

Who were looking for

Must-have

• Strong applied computer vision / deep learning experience. Youve trained fine-tuned and debugged CV models not just consumed APIs. You understand whats happening inside the models you use.
• Hands-on with the experimental loop: dataset curation augmentation training error analysis iteration. Youre 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 dont 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) youll get plenty of opportunities to use it.
• Weakly- or self-supervised learning active learning loops.

How youll work

• You report to the Head of Product & Engineering. Coaching and technical sparring with the Engineering Lead.
• Youll work in cross-functional squads with platform engineers and our product team.
• Youll 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. Youll grow across the stack as much as you want to.
• Strategic seat. AI is central to where Sitemark is going. Youll 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 doesnt.

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.