AI/ML Engineer

Role Overview

• 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.
• 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.
• 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.

Tech Stack

• Postgres
• Python
• React
• TypeScript

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.