Engineering Lead Human Influence
UK government research organisation that evaluates advanced AI risks and develops and tests mitigations to inform governments.
About AI Security Institute
The AI Security Institute (AISI) is a research organisation within the UK Department for Science, Innovation and Technology. It conducts technical research, evaluates leading AI systems, develops risk mitigations, and shares evaluation infrastructure such as Inspect.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead and grow an engineering function, define an engineering roadmap, and manage research and software engineers. You will deliver scalable systems, data pipelines, multi-agent study tooling, model fine-tuning workflows, and reinforcement-learning environments for research and evaluations.
Requirements
- Experience leading a complex engineering or research project from scoping through deployment or publication
- Understanding of how AI and coding assistants affect engineering and research-team work
- Understanding of current AI safety literature
- Experience translating research questions into reproducible engineering tasks
- Experience supporting a product or research roadmap with non-technical stakeholders
- Software design knowledge for cloud deployments and databases
- Experience with Docker, Kubernetes, or AWS
- Experience writing scalable, maintainable production Python code
- Experience building or fine-tuning machine-learning models using PyTorch, Keras, JAX, or custom code
- Experience with frontend TypeScript and Node.js deployments
- At least 1 year of line-management experience
- Experience hiring and mentoring technical staff
Responsibilities
- Lead and grow the engineering function
- Manage research and software engineers
- Develop and deliver an engineering roadmap
- Design scalable architecture for model evaluations and benchmarks
- Build automated pipelines for human-AI experiments
- Design and build tooling for multi-agent studies
- Deliver engineering-heavy research projects
- Build robust data pipelines
- Fine-tune LLMs and build reinforcement-learning environments
Benefits
- Pre-release access to frontier models and ample compute
- Hybrid working and flexibility for occasional remote work abroad
- Stipend for work-from-home equipment
- At least 25 days of annual leave
- 8 public holidays
- Extra team-wide breaks
- 3 days of volunteering leave
- Paid parental leave
- Employer pension contribution of 28.97% of base salary
- Cycling, donation, retail, and gym discounts and benefits
