AI Engineer

h3bAbout Sitemark /b /h3 pSitemark is the AI and robotics platform for building and operating renewable energy. The world’s leading Owners, EPCs, and OM companies use Sitemark. /p pbWhat problem are we solving and why is this important to solve: /b /p pThe 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. /p pbThe team you’ll join: /b /p pYou 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. /p pbHow we operate: /b /p ul lipWe care deeply about our customers and the problems we solve for them. /p /li lipWe move fast, keep things simple, and focus on what matters. /p /li lipWe keep our quality bar high by staying lean and hiring only the best. /p /li /ul h3bAbout the role /b /h3 pAt 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. /p pAs 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. /p pWe’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. /p pYou’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. /p pbWhat you’ll do: /b /p ul lipTrain, 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. /p /li lipLevel 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. /p /li lipRun the full experimental loop end to end: curate and improve datasets, design training runs, analyse errors, and iterate. /p /li lipTake 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. /p /li lipIntegrate 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. /p /li lipChoose problems and approaches based on business impact — what actually moves the needle for our products and operations. /p /li /ul pbWhat success looks like after 6–12 months: /b /p ul lipYour models are running in production on real data, moving the metrics our customers and operations teams care about. /p /li lipThe MLOps backbone is solid — training is reproducible, experiments are tracked, and shipping a new model is routine, not a fire drill. /p /li lipA feedback loop runs from labeled operations data back into training, so the models keep getting better on their own cadence. /p /li lipThroughput across model work, training runs, and dataset iteration is meaningfully higher than when you started. /p /li lipAI is unblocking the roadmap, not bottlenecking it — the gaps we hired you to close are closed. /p /li /ul h3bRequirements /b /h3 ul lipStrong 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. /p /li lipHands‑on with the experimental loop: dataset curation, augmentation, training, error analysis, iteration. When results are bad, you know how to diagnose why. /p /li lipA pragmatic, product‑oriented mindset — you reason about how a model w