Founding Machine Learning Researcher (LLM Post-Training)

SR2; Greater London; Hybrid; GBP 70,000 - 120,000; Full time

Benefits offered by this job

Equity

Hybrid work model

Job summary

SR2 in London is seeking a Research Scientist/Engineer to lead large-scale experimentation on continual learning. You will own research questions and design training programs for multi-billion-parameter language models, working directly with a small, highly technical team at an early stage.

The role offers ownership over both research and implementation and comes with a highly competitive salary and equity.

Qualifications

• Substantial hands-on experience training or post-training language models.
• Experience with SFT and reinforcement learning for LLMs.
• Knowledge of modern policy-optimisation methods.
• Experience with distributed or multi-GPU training.
• Strong Python and PyTorch, JAX or equivalent framework experience.
• Understanding of training dynamics, evaluation and generalisation.
• Ability to own research directions rather than only implement predefined approaches.

Responsibilities

• Train and post-train large language models.
• Design supervised fine-tuning and reinforcement-learning experiments.
• Research continual and sequential model adaptation.
• Build datasets, curricula and training-data mixtures.
• Evaluate capability improvements, retention and regressions.
• Work with distillation, synthetic data and model-generated training data.
• Develop RL environments and verifiable-reward tasks.
• Diagnose training failures and unexpected model behaviour.
• Build reliable experimentation and evaluation infrastructure.
• Collaborate with researchers developing new learning algorithms.

Skills

LM training

SFT

RL for LLMs

Distributed training

Python

PyTorch

JAX

Education

PhD preferred

Tools

PyTorch

JAX

Job description

London - Hybrid

I'm hiring for an AI start-up focused on continual learning. They're looking for a Research Scientist/ Engineer to lead large-scale experimentation across supervised fine-tuning, reinforcement learning, distillation and repeated model adaptation.

You will take ownership of research questions, design training programmes and work directly with multi-billion-parameter language models.

What you’ll work on

• Train and post-train large language models.
• Design supervised fine-tuning and reinforcement-learning experiments.
• Research continual and sequential model adaptation.
• Build datasets, curricula and training-data mixtures.
• Evaluate capability improvements, retention and regressions.
• Work with distillation, synthetic data and model-generated training data.
• Develop RL environments and verifiable-reward tasks.
• Diagnose training failures and unexpected model behaviour.
• Build reliable experimentation and evaluation infrastructure.
• Collaborate with researchers developing new learning algorithms.

What we’re looking for

• Substantial hands-on experience training or post-training language models.
• Experience with SFT and reinforcement learning for LLMs.
• Knowledge of modern policy-optimisation methods.
• Experience with distributed or multi-GPU training.
• Strong Python and PyTorch, JAX or equivalent framework experience.
• Understanding of training dynamics, evaluation and generalisation.
• Ability to own research directions rather than only implement predefined approaches.

Particularly valuable experience

• Working with models in the 10B+ parameter range.
• Continual learning or repeated model adaptation.
• Distillation and synthetic-data generation.
• Reinforcement learning with verifiable rewards.
• Dataset construction and curriculum design.
• Building evaluation systems that detect capability loss and regression.

A PhD is valuable but not essential. Direct evidence of successfully training, adapting and evaluating large models is the primary requirement.

• Work on fundamental problems in language-model adaptation.
• Run ambitious experiments with direct influence over the research roadmap.
• Join a small, highly technical team at an early stage.
• Take meaningful ownership over both research and implementation.
• Receive a highly competitive salary and significant equity.