Member of Technical Staff - Research Scientist

General Reasoning, Inc.; Greater London; Vor Ort; GBP 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading AI research firm in London is seeking a Research Scientist to push the frontier of reinforcement learning. The role involves designing, implementing, and evaluating novel RL methods for language models and requires experience in reinforcement learning and proficiency in Python. You will collaborate closely with engineering teams, run large-scale experiments, and publish findings to contribute to the advancement of AI. A competitive salary package is offered in a fast-paced research environment.

Qualifikationen

• Strong background in reinforcement learning: policy gradient methods, value-based methods, PPO, or related algorithms.
• Proficiency in Python and ML frameworks with the ability to implement and run experiments independently.
• Evidence of extraordinary accomplishment in work or early life.

Aufgaben

• Design and implement novel RL algorithms and training methodologies for long-horizon agents.
• Run RL training experiments at scale and analyse agent behaviour.
• Collaborate with engineering teams to bring research innovations into production.

Kenntnisse

Reinforcement learning

Python

ML frameworks (PyTorch or JAX)

Policy gradient methods

Value-based methods

Jobbeschreibung

Member of Technical Staff Research Scientist

Push the frontier of reinforcement learning for long-horizon work

London, UK Full-time Research

Context

Reinforcement learning is how language models learn to reason, use tools, and work autonomously over long horizons. But RL for long-horizon agents presents new challenges, including off-policyness of long trajectories, sparse credit assignment, and training instability.

At General Reasoning, RL research is core to what we do. We train agents on environments hosted on OpenReward and need researchers who can design and implement novel RL methods, run experiments at scale, and push the capabilities of long-horizon agents. Before GR, our team previously worked on some of the leading open-source language model efforts and early reinforcement learning work on language models.

About this Role

As a Research Scientist, you'll design, implement, and evaluate agentic reinforcement learning methods for language models. You'll work across the full research loop - formulating problems, building training pipelines, running experiments, analysing results, and publishing your work. Your research will directly shape how agents are trained on the OpenReward platform.

Design and implement novel RL algorithms and training methodologies for long-horizon language model agents

Develop and iterate on reward signals, shaping methods, and credit assignment strategies for long-horizon tasks

Run RL training experiments at scale, analyse agent behaviour, and debug failure modes such as reward hacking and training instability

Build and maintain research infrastructure: training pipelines, experiment tracking, and evaluation harnesses

Publish papers and engage with the broader RL and LLM research community

Collaborate closely with the engineering team to bring research innovations into production training

Requirements

We're looking for exceptional individuals who can operate at the frontier of AI research and engineering.

Obsessive attention to detail, high intensity and focus

Evidence of extraordinary accomplishment (either in work or early life)

Kind, self-aware and collaborative team player

Strong background in reinforcement learning: policy gradient methods, value-based methods, PPO, or related algorithms

Experience training RL agents and iterating on reward design, with strong intuitions for what works and why

Proficiency in Python and ML frameworks (PyTorch or JAX) with the ability to implement and run experiments independently

Track record of research output: publications, open-source contributions, or impactful technical projects

Willingness to work in-person in London, UK

Nice to Have

Experience applying RL to language models with verifiable rewards (RLVR)

Experience with long-horizon or multi-step RL tasks and the specific challenges they present

Experience with distributed training infrastructure and running experiments at scale on GPU clusters

Background in LLM training methodologies (pre-training, post-training, fine-tuning)

Experience working at a frontier lab on critical path research

About General Reasoning

General Reasoning (GR) was founded by ex-frontier lab researchers who were at the forefront of the LLM revolution. We've built models, products and evaluations used by hundreds of millions of people, and have extensive experience across the LLM stack. We are backed by pioneers in the field including the creators of PyTorch and TensorFlow.

We are well-funded and have recently launched OpenReward, the platform for serving RL environments at scale.