Founding Research Scientist – Adaptive AI & RL, Equity

Stealth Startup; Greater London; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Equity 1.2%–3%

Job summary

Stealth Startup in London is building an early-stage AI company focused on adaptive intelligence and reinforcement learning. You’ll help shape the research direction, core technology and engineering foundations with substantial autonomy to identify important problems and lead initiatives, with potential for leadership roles as the company grows.

Depending on your interests, you’ll contribute to adaptive intelligence, RL and post-training, continual learning, evaluation and generalisation, and

Qualifications

• Strong ML foundations with model architectures, training methods and evaluation.
• Hands-on programming in Python and ML frameworks.
• Experience conducting ML research or building sophisticated ML systems.
• Ability to work independently with limited direction.
• Scientific rigour and willingness to challenge existing approaches.
• Genuine interest in building a company, not just a research project.
• 2+ years of experience at a frontier AI lab or high-impact startup.

Responsibilities

• Shape the research direction, core technology and engineering foundations.
• Lead research initiatives and turn promising results into real-world systems.
• Collaborate with founders to shape technical culture and strategy.
• Potential to take on research or engineering leadership as the company grows.

Skills

ML foundations

Python

PyTorch

Research leadership

Education

PhD or MSc in AI/ML

Tools

TensorFlow

JAX

Job description

Stealth Startup in London is building an early-stage AI company focused on adaptive intelligence and reinforcement learning. You’ll help shape the research direction, core technology and engineering foundations with substantial autonomy to identify important problems and lead initiatives, with potential for leadership roles as the company grows.

Depending on your interests, you’ll contribute to adaptive intelligence, RL and post-training, continual learning, evaluation and generalisation, and