Senior Full-Stack Engineer (ML & Data) Stockholm - Hybrid

About this role

Senior Full-Stack Engineer (ML & Data) Own our prediction models from training pipeline to customer-facing feature. Strong applied ML — calibration, evals, leakage — plus full-stack TypeScript to ship it. We build AI agents that audit and optimize e-commerce advertising. Our platform syncs live data from Google Ads, Meta, Merchant Center, and Google Analytics, runs it through a rule engine, LLM-powered audit agents, and our own prediction models, and turns findings into concrete actions marketers can apply with one click.

What sets us apart is measurement: we connect real profit data from our customers' e-commerce platforms to their ad accounts and build models that tell them what their marketing actually returns — not what ad platforms self-report. The ML in our product isn't decoration. Prediction models, calibration, and evaluation pipelines are core to what customers pay for. We're a small team shipping fast on a modern, strictly-typed stack.

You'd report directly to the CTO, with real ownership: features you design end-to-end, and models you own from training data to the number a customer sees on screen. The role This is a hybrid role — roughly 60% product engineering, 40% ML & data science. You'll ship full-stack features in our TypeScript monorepo and own prediction models as production software: framing the problem, building training and evaluation pipelines, monitoring calibration drift, and wiring outputs into the product.

We're not looking for a research scientist, or a pure web engineer. We want someone who treats a model the way a good engineer treats a service: tested, monitored, versioned, and honest about its failure modes. What you'll do Own prediction models end-to-end — models that predict product returns and net profit per order — training data pipelines, feature engineering, evaluation (ranking quality and calibration), recalibration strategies, and serving model output into the product.

Build and maintain data pipelines — SQL feature builders on PostgreSQL shared by training and serving, versioned dataset exports, backfills, and data quality checks that keep training data honest (no leakage, no silent schema drift). Design and build features across our TypeScript monorepo — tRPC procedures and NestJS services on the backend, React on the frontend — so model insights become actions marketers can apply with one click.