Axiōma Search
Member of Technical Staff – RL Environments / Evals
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Member of Technical Staff – Environments / Evals
Stealth AI Lab | Paris or London (Hybrid)
About
AI models need useful problems to practise on if they're going to improve. This role is about creating those problems automatically — and then building evaluations that tell you whether the model has genuinely learned something.
The company is building AI systems that learn how to carry out complex work inside large organisations. They recreate real-world workflows as interactive training environments, then use those environments to train models through practice and feedback — so the models get better at completing long, multi-step tasks reliably, rather than simply generating answers.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
You'll build systems that generate training tasks, adjust their difficulty as models improve, and simulate parts of real-world deployments. You'll also make sure improvements are real rather than models exploiting shortcuts in the training environment.
What you'll do
- Build pipelines that automatically generate tasks and training environments
- Create systems that filter tasks for validity, diversity and difficulty
- Adjust training curricula based on where models succeed and fail
- Train simulators using data from real model deployments
- Test how closely simulated environments match real interactions
- Build reproducible evaluation frameworks and held-out tests
- Identify reward hacking, shortcuts, contamination and failures to generalise
Get help with your application
Your very own career expert that helps elevate your application to the next level.
What you'll need
- Strong engineering skills and good experimental judgement
- Experience with PyTorch and modern ML systems
- Experience in synthetic data, agents, world models, curriculum learning or evaluation
- Ability to design rigorous model evaluations
- Strong understanding of model behaviour and failure analysis
- Comfort working across open-ended research and engineering problems
Optional
- Ray, Docker, OpenEnv or Gymnasium
- vLLM, SGLang, Playwright, Inspect AI or DeepEval
Shortlisted candidates will be contacted within 48 hours.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Location