About this Project Technical Lead - AI Systems Simulation role at CommonAI C.I.C.
CommonAI is redefining deep-tech AI solutions through collaborative engineering and shared infrastructure. Working at the intersection of AI, cloud, and large-scale infrastructure, we partner with commercial, government, and research entities to accelerate AI adoption across the UK and Europe. Backed by UK Government and private funding, our team of experienced founders and engineers is building the future of AI systems.
The Role
As AI models scale, understanding the real-world impact of hardware, systems software, and architecture on performance, cost, and energy is critical. CommonAI’s £70m+ Scaling Inference programme is developing a comprehensive simulation toolkit to model inference systems across the stack.
We are seeking a Project Technical Lead to spearhead this initiative. In this leadership role, you will define the technical strategy for our simulation platform, coordinating cross-functional workstreams to bridge the gap between hardware architecture and model workloads. You will be responsible for the programme's overall technical direction, setting standards for excellence, and ensuring the research outputs align with our strategic mission.
Key Responsibilities
- Technical Strategy & Vision: Own the architectural roadmap for the Scaling Inference simulation platform, ensuring technical decisions support long-term goals.
- Workstream Coordination: Manage and synchronise development efforts across hardware, software, and model workload workstreams to deliver a cohesive simulation toolkit.
- Technical Standards & Quality: Establish and enforce engineering best practices, rigorous validation methodologies, and performance standards across the team.
- Stakeholder Management: Act as the primary technical point of contact for the Scaling Inference programme, communicating progress and technical risks to senior leadership and partners.
- Mentorship & Growth: Provide technical guidance and mentorship to research engineers, fostering a culture of innovation and continuous professional development.
- Project Delivery: Lead the planning and execution of experimental cycles, ensuring the Scaling Inference Lab outputs are integrated into the core platform efficiently.
- Resource Allocation: Prioritise technical tasks and research questions to maximise programme impact on cost, performance, and energy efficiency targets.
- Documentation: Communicate findings through technical reports, internal publications, and external research outputs.
Requirements
Essential
- Technical Leadership: Proven experience leading technical teams or managing complex engineering projects in a research or high-growth environment.
- Strategic Communication: Ability to translate complex technical concepts for diverse stakeholders and influence technical strategy at a programme level.
- Advanced degree (PhD preferred) in Computer Science, Engineering, or a related field with deep domain knowledge in AI systems and hardware architecture.
- Track record of delivery, moving sophisticated technical prototypes through to production-grade, scalable systems.
- Experience with GPU optimisation, performance modeling, and the simulation of large-scale infrastructure workloads.
Desirable
- Experience managing cross-functional teams in AI infrastructure, ML systems, or computer architecture.
- Familiarity with Agile or other modern technical project management frameworks.
- Knowledge of modern inference-serving frameworks (e.g., vLLM).
- Background in statistics, operations research, or large-scale datacenter infrastructure.
- Contributions to open-source AI or systems projects.
Benefits
Benefits
- High-impact role in a rapidly growing, well-funded organisation.
- Collaborative and supportive work environment.
- Competitive salary, pension, healthcare, and benefits.
- Networking opportunities with leaders across tech and academia.
- Vibrant office situated near to Cambridge train station.
CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace.