TL;DR - Push our verification research forward and turn ideas into signals that ship. You will prototype approaches, build the evaluation that shows whether they work, and hand the winners to production together with the engineers who run it.
What you will do
- Explore and prototype new detection and analysis approaches for AI-generated and manipulated content across images, video, documents, and other media
- Build datasets, benchmarks, and evaluation that reflect real fraud and trust-and-safety scenarios rather than lab conditions
- Work with engineering to take the approaches that hold up into production, with results customers can explain and defend
What we are looking for
- 2+ years of hands-on applied ML or computer vision work, in research or industry, with code and results you can show
- Experience designing evaluation to a scientific standard: out-of-distribution test sets, strong baselines, and ablations
- Clear written and spoken communication of results to engineers and non-specialists
Nice to have
- Publications, open-source models, or benchmarks you have released
- Experience with media forensics or synthetic content detection
This is Pixel Systems
Pixel Systems builds verification infrastructure for high-stakes decisions. When an image, video, or document arrives from a source a business does not control, Pixel tells the team whether it can be trusted enough to act on: a risk score, the signals behind it, and a record that stands up under scrutiny. Insurers, marketplaces, media, legal, and finance teams use it inside the workflows they already run.
- How we work: a small engineering team in Stockholm where each engineer owns what they build from design through production, with AI-assisted tooling as part of the daily workflow
- What you get: market-rate salary, equity, and work on one of the defining problems of the next decade, keeping evidence trustworthy when anyone can fabricate it
Our tech stack
We build on modern cloud infrastructure and AI-assisted tooling so engineers can move fast, stay in flow, and spend their time on problems worth owning.
- Frontend: React, TypeScript
- Backend: Python, C#, TypeScript
- ML: TensorFlow, PyTorch, ONNX, MLflow, dedicated hardware compute
- Hosting: managed cloud, infrastructure as code
- DevOps & tooling: GitHub Actions, OpenTelemetry, Terraform
How to apply
- Apply in English, our working language day to day and the one you will use most if you join.
- We read every application ourselves and treat each one on its merits. If this role fits, submit through this careers page.
- If the role fits but you do not tick every point, apply anyway. We would rather read your application than miss it.