Enigma
Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pre-Training | Long-Context Reasoning | London, Full-Time
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Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pre-Training | Long-Context Reasoning | London, Full-Time
Member of Technical Staff
About the Company
We are a frontier AI research and product company building the next generation of autonomous and interpretable AI systems.
Founded by an experienced team of researchers and operators from leading AI organisations, we are rapidly scaling our research, engineering, and product efforts. Our goal is to build one of the highest-performing and most talent-dense AI teams in Europe.
Based in London, we are seeking ambitious and highly capable individuals who share our vision of a future where humans interact continuously, safely, and productively with autonomous AI agents. We value self-starters who can take ownership, move quickly, and thrive in a fast-paced, high-growth environment.
The Role
Members of the Technical Staff operate as high-agency generalists. You will be expected to:
- Own projects end-to-end
- Contribute across multiple initiatives as needed
Given our focus on the frontier of multiple technical domains, the ability to learn quickly and adapt on the job is essential.
We are currently a small but rapidly growing team, so early hires will play a key role in shaping both the technical direction and company culture. The organisation operates with a flat structure, providing significant autonomy and ownership.
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We are hiring across a range of seniority levels, from experienced technical experts to driven early-career candidates.
What You Might Work On
We are hiring for both deep technical specialists and exceptionally fast-learning generalists. Potential areas of work:
-
Memory, Retrieval, and Long-Context Reasoning
- Learned retrieval systems
- Fast adaptation and meta-learning
- KV-cache reuse and compression
- Context distillation
- Unified retrieval and reasoning systems
-
Agentic Systems, Reinforcement Learning, and Self-Improvement
- Tool use and multi-step reasoning
- Preference learning and reward modelling
- LLM-as-judge systems
- Process reward models
- Synthetic trajectory generation
- Offline-to-online reinforcement learning
- Reinforcement Learning in unverifiable domains
-
World Models, Simulators, and Optimisation Loops
- Learned simulators of real deployment environments
- Agents that propose experiments and optimise prompts, policies, and data pipelines
- Agentic approaches to data science and experimentation
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- Infrastructure, Training, and Observability
- Distributed training systems
- Efficient inference and quantisation
- Observability, auditing, and rollback tooling for safe deployment
About You
We’re looking for:
- Experience at a leading research lab, high-growth startup, or similarly ambitious and fast-paced environment
- The ability to move quickly with a track record of outsized results in short periods of time
- A first-principles approach to identifying what the company needs, beyond immediate tasks
- Ambition, competitiveness, and motivation to solve difficult problems
- A self-starter who thrives in limited supervision or incomplete specifications
Practicalities
- Location: London-based, with a preference for in-person collaboration and culture-building.
- Compensation: Competitive salary and equity package.
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