Engineering Lead, AI Safety & Research Systems

Aisi; Greater London; Hybrid; GBP 65,000 - 145,000; Full time

Benefits offered by this job

Hybrid working

Learning stipend

Conference funding

Pension contribution

Job summary

AI Security Institute seeks an Engineering Lead to manage a growing team of engineers. The role drives scalable evaluation pipelines, multi-agent research, and production-grade code for large-scale AI safety studies.

You will collaborate with HI leadership to chart an engineering roadmap and mentor researchers and software engineers. Strong candidates will have experience delivering complex projects from design to deployment, plus hands-on work with ML systems, cloud deployments, and

Qualifications

• Experience leading engineering or research projects from scoping to deployment/publication.
• Ability to translate research questions into reproducible engineering tasks.
• Knowledge of cloud deployments and production code practices.
• Experience shipping ML models or LLMs using common libraries.

Responsibilities

• Design scalable architecture for repeatable evaluation delivery and benchmarking.
• Build automated pipeline to recruit participants and run human-AI experiments end-to-end.
• Develop toolkit for multi-agent studies with performance guarantees.
• Lead engineering-heavy research projects on Human Influence topics, including post-training techniques on large compute clusters.

Skills

Engineering leadership

Cloud deployments

Python production code

LLM finetuning

Team management

Research collaboration

Tools

Docker

Kubernetes

AWS primitives

PyTorch

Keras

Job description

AI Security Institute seeks an Engineering Lead to manage a growing team of engineers. The role drives scalable evaluation pipelines, multi-agent research, and production-grade code for large-scale AI safety studies.

You will collaborate with HI leadership to chart an engineering roadmap and mentor researchers and software engineers. Strong candidates will have experience delivering complex projects from design to deployment, plus hands-on work with ML systems, cloud deployments, and