Senior Software Engineer, Simulation And Forecasting

Turn AI research into simulations and forecasts that decision-makers can act on, and measure them against what really happens.

About Serinyx

Serinyx builds AI for high-stakes decisions on confidential data.

On our decision-intelligence platform, AI agents break a hard question down, research it with checked citations and debate the findings under human review.

The result is a verified, cited and signed report.

The role

You turn AI-generated research into decisions people can act on: the report they read, and the world model behind it.

Here, a world model is an executable simulation of the decision, with its causes, effects and uncertainties.

It tests scenarios and forecasts what will happen.

• Why this role
• You build the part of the product that turns evidence into a decision.
• Your forecasts are scored against real outcomes, so their quality is measured, not claimed.
• Simulation, probabilistic forecasting and LLMs in one role, from the model to the report.

What you will do in your first year

• Build the report layer: report formats, charts of the evidence, digitally signed output, and an assistant that answers questions about a finished report.
• Build the engine that creates world models from research results, with parameter ranges, cause-and-effect links and scenarios, validated against reference data.
• Add calibrated forecasts: questions registered in advance, probability forecasts with measured quality (for example Brier scores), and comparison with what actually happened.

What you bring

• Several years designing and owning production software in Python. Show us one.
• Quantitative modelling: simulation (Monte Carlo, system dynamics or agent-based), probabilistic forecasting, uncertainty.
• Turning model output into clear reports and charts.
• Comfort with LLM pipelines and with directing AI coding agents: you turn a vague goal into a precise work order an agent can build from, and you can show us a time you caught an agent’s code or tests being wrong.
• Nice to have

Bayesian modelling (Py MC, Stan); decision analysis; forecasting tournaments or platforms; document generation (PDF, HTML); domain knowledge in energy, industry or finance.

• How we work
• One team, one backlog. Everyone builds from day one and works directly with our CTO, Dr Raja Naeem Akram.
• Owners, not ticket-takers.

Each engineer owns parts of the codebase and leads the AI coding agents working in them: writing their work orders, reviewing every pull request and deciding what merges.

• A typical week. Much of your time goes into specifying and reviewing work done by AI coding agents; the hardest parts you write yourself.
• AI agents take the routine platform work.

Infrastructure, deployment and monitoring are done by AI agents under engineering review, so your time goes into design and the hard problems.

You still own what you build once it is live.

• Quality is part of the job.

You write the tests for your own work and keep CI green.

Done means tests pass, security scans are clean, docs are updated and container images are signed.

• Two-week cycles. Planning on day one, a demo on day ten and a short written retro.
• English first. English is our working language; German is welcome but not required.

What we offer

• Real ownership. An early role in a small team: you shape the product, the code and how we work, not a ticket queue.
• A modern stack. Python, Fast API, Temporal, Kubernetes, React with Type Script, and the best AI models for each task.
• Fully remote.
• 30 days of holidays per year.
• International applicants welcome. If you come from outside the EU, we support you through the work-permit process, usually the EU Blue Card.
• Equal opportunity
• We welcome applications from everyone, whatever their ethnic origin, gender, religion or belief, disability, age or sexual identity.

You don’t need to include a photo or your date of birth.