Technology, Information and Internet

Sitemark

11-50

Happy Holidays 2024

What we are building at Sitemark
Sitemark is the AI and robotics platform for building and operating renewable energy. The world's leading Owners, EPCs, and O&amp;M companies use Sitemark.
What problem are we solving and why is this important to solve
The world is building one of the largest infrastructure shifts in history — and it needs better tools to get it done. Renewable energy projects are growing in scale, complexity, and urgency, but the teams responsible for building and operating them are stretched thin — buried in repetitive work, disconnected systems, and running out of time. That's why we built Sitemark: AI and robotics take the repetitive load off these teams, from the field to the office, so their time goes to what matters — and sites get built faster and perform better.
The team you'll join
You will join an extremely talented team of incredibly passionate, high-energy people. We go the extra mile while having the best time of our lives.
How we operate
<ul>
<li>We care deeply about our customers and the problems we solve for them.</li>
<li>We move fast, keep things simple, and focus on what matters.</li>
<li>We keep our quality bar high by staying lean and hiring only the best.</li>
</ul>
<h2>Tasks</h2>
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 &amp; 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
<ul>
<li>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.</li>
<li>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.</li>
<li>Run the full experimental loop end to end: curate and improve datasets, design training runs, analyse errors, and iterate.</li>
<li>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.</li>
<li>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.</li>
<li>Choose problems and approaches based on business impact — what actually moves the needle for our products and operations.</li>
</ul>
What success looks like after 6–12 months:
<ul>
<li>Your models are running in production on real data, moving the metrics our customers and operations teams care about.</li>
<li>The MLOps backbone is solid — training is reproducible, experiments are tracked, and shipping a new model is routine, not a fire drill.</li>
<li>A feedback loop runs from labeled operations data back into training, so the models keep getting better on their own cadence.</li>
<li>Throughput across model work, training runs, and dataset iteration is meaningfully higher than when you started.</li>
<li>AI is unblocking the roadmap, not bottlenecking it — the gaps we hired you to close are closed.</li>
</ul>
<h2>Requirements</h2>
<ul>
<li>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.</li>
<li>Hands-on with the experimental loop: dataset curation, augmentation, training, error analysis, iteration. When results are bad, you know how to diagnose why.</li>
<li>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.</li>
<li>Strong fundamentals and clean engineering instincts. You write code meant to live in production — readable, testable, maintainable — not just notebook scratch.</li>
<li>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.)</li>
<li>High intelligence and learning velocity — we care more about how you think and how fast you grow than years on a CV.</li>
<li>Comfortable working in English in a small, fast-moving team.</li>
</ul>
Big plus:
<ul>
<li>Aerial / drone / remote-sensing imagery (orthomosaics, geo-referencing, multi-band, large images).</li>
<li>Non-visual imagery (thermal, multispectral).</li>
<li>Detection, segmentation, keypoint, or multi-scale architectures applied to large or high-resolution images.</li>
<li>Production MLOps: experiment tracking, reproducible training, model registries, monitoring.</li>
<li>Full-stack experience (Python, TypeScript, React, Postgres) — you'll get plenty of chances to use it.</li>
<li>Weakly- or self-supervised learning, active-learning loops.</li>
</ul>
<h2>Benefits</h2>
<ul>
<li>Real impact, fast: a clearly identified gap, a concrete roadmap, and customers waiting on the results. Your models will ship.</li>
<li>Breadth: from datasets and model work through MLOps and into product integration — you'll grow across the stack as much as you want.</li>
<li>A strategic seat: AI is central to where Sitemark is going, and you'll help shape that direction, not just execute on it.</li>
<li>A pragmatic culture: we care about results, not theatre — the boring solution when it works, the hard one when it doesn't.</li>
<li>Work on a mission that matters: accelerating the world's transition to renewable energy.</li>
<li>Competitive compensation including meaningful equity (stock options) with real upside.</li>
<li>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.</li>
</ul>

AI/ML Engineer

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