At a Glance
- Tasks: Enhance AI models post-training using reinforcement learning and improve experimental systems.
- Company: Leading AI lab focused on innovative solutions for large organisations.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and innovation.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: Strong programming skills, experience with PyTorch, and knowledge of reinforcement learning.
The predicted salary is between 59400 - 72600 £ per year.
- Member of Technical Staff – Post-training / RL
- Stealth AI Lab | London or Paris (Hybrid)
- About
This role is about making models better after their initial training.
You'll work on reinforcement learning and other post-training methods, while also improving the systems needed to run those experiments efficiently at scale.
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.
You'll work across both the learning algorithms and the infrastructure underneath them.
That means going from an RL experiment to a GPU profiler trace, finding what's limiting performance, and making sure systems improvements don't change the way the model learns.
- What you\'ll do
- Build and improve supervised fine-tuning, preference optimisation and RL methods
- Work with approaches including PPO, GRPO and SDPO
- Own training loops from rollout generation through to policy updates and checkpointing
- Improve training throughput, GPU utilisation and memory efficiency
- Profile and fix bottlenecks across distributed training
- Investigate instability and differences between training and inference
- Use real model failures to improve rewards, training data and overall performance
- What you\'ll need
- Strong programming and quantitative problem-solving skills
- Hands-on experience with Py Torch and model training
- Understanding of reinforcement learning or LLM post-training
- Experience with distributed training and GPU systems
- Strong experimental judgement
- Ability to work comfortably across ML research and systems engineering
- Optional
- v LLM, SGLang, Ray, FSDP or Megatron-LM
- CUDA, Triton, Cu TE or GPU performance optimisation
Shortlisted candidates will be contacted within 48 hours.
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Research Engineer (Post-Training) employer: Axiōma Search
Axiōma Search is an exceptional employer for those passionate about machine learning and time-series forecasting. With a collaborative work culture that prioritises innovation and employee growth, team members are encouraged to explore new ideas and technologies while benefiting from a global network of experts. Located in Europe, the company offers unique opportunities for professional development and the chance to make a significant impact in the field of AI.
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We think this is how you could land Research Engineer (Post-Training)
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We think you need these skills to ace Research Engineer (Post-Training)
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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