TL;DR

Research Engineer Scientist (AI): Build and run machine learning experiments to understand and steer powerful AI systems with an accent on AI safety, alignment, and robustness. Focus on designing and executing empirical research, testing safety techniques, and collaborating across teams to address challenges in advanced AI behavior.

Location: Must be based at least 25% in London and travel occasionally to San Francisco

Salary: £250,000 - £270,000 GBP annually

Company

%hirify_global% is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems that are safe and beneficial for society.

What you will do

Build and run machine learning experiments to study AI system behavior and safety.
Test robustness of safety techniques by training language models to subvert interventions.
Run multi-agent reinforcement learning experiments and build evaluation tooling.
Write scripts and prompts to generate evaluation questions for safety-relevant reasoning.
Contribute to research papers, blog posts, and talks on AI safety topics.
Collaborate with teams including Interpretability, Fine-Tuning, and Frontier Red Team.

Requirements

Must be based at least 25% in London and able to travel to San Francisco occasionally
Bachelor's degree or equivalent experience in a related field
Significant software, machine learning, or research engineering experience.
Familiarity with empirical AI research and technical AI safety.
Collaborative mindset and willingness to take on tasks beyond job description.
Care about the impacts of AI and AI safety challenges.

Nice to have

Experience authoring research papers in machine learning, NLP, or AI safety.
Experience with large language models (LLMs) and reinforcement learning.
Experience with Kubernetes clusters and complex shared codebases.

Culture & Benefits

Competitive compensation including equity and benefits.
Visa sponsorship available with immigration lawyer support.
Flexible working hours and hybrid work policy.
Generous vacation and parental leave.
Collaborative and impact-driven research environment.