Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pr[...]

Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pr[...]

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Enigma

At a Glance

  • Tasks: Own and drive innovative AI projects in a fast-paced environment.
  • Company: Join a cutting-edge AI research company shaping the future of autonomous systems.
  • Benefits: Competitive salary, equity package, and opportunities for personal growth.
  • Other info: Flat structure offering autonomy and significant influence on company culture.
  • Why this job: Be part of a talented team making a real impact in AI technology.
  • Qualifications: Experience in high-growth environments and a passion for solving complex problems.

The predicted salary is between 60000 - 80000 Β£ per year.

Company Overview

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 looking for 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 Technical Staff operate as high-agency generalists. You will be expected to own projects end-to-end while also contributing across multiple initiatives as needed. Because we work at the frontier of several technical domains, the ability to learn quickly and adapt on the job is essential. We are currently a small but rapidly growing team. Early hires will play a key role in shaping both the technical direction and company culture. We are hiring across a range of seniority levels, from experienced technical experts to highly driven early-career candidates. The organisation operates with a flat structure and provides significant autonomy and ownership.

What You Might Work On

  • Memory, Retrieval, and Long-Context Reasoning
  • Agentic Systems, Reinforcement Learning, and Self-Improvement
  • World Models, Simulators, and Optimisation Loops
  • Infrastructure, Training, and Observability

About You

You have experience at a leading research lab, high-growth startup, or similarly ambitious and fast-paced environment. You move quickly and have a track record of producing outsized results in short periods of time. You think from first principles about what the company needs, not just the task immediately in front of you. You are ambitious, competitive, and motivated by solving difficult problems. You are a self-starter who operates effectively with limited supervision or incomplete specifications.

Practicalities

Location: London-based, with a strong preference for in-person collaboration and culture-building. Compensation: Competitive salary and equity package.

Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pr[...] employer: Enigma

Enigma is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong focus on employee growth, we provide ample opportunities for professional development and hands-on experience in cutting-edge technologies within the healthcare sector. Our commitment to reliability, security, and privacy compliance ensures that you will be part of a meaningful mission, making a real impact on clinical monitoring and patient care.

Enigma

Contact Details:

Enigma Recruitment Team

We think you need these skills to ace Member of Technical Staff | Large Language Models | Reinforcement Learning | Post-Training | Pr[...]

Reinforcement Learning
Large Language Models
Long-Context Reasoning
Memory Systems
Meta-Learning
Agentic Systems
Preference Learning