Founding AI Accelerator Architect (Research & Hardware Co-Design)

• A strong research or engineering background in computer architecture, digital hardware, AI accelerators, or hardware–software co-design.
• Ideally a PhD or equivalent research experience in computer engineering, electrical engineering, computer science, or a related field.
• Experience designing, implementing, or evaluating hardware accelerators for neural networks or other computationally intensive workloads.
• A deep understanding of digital architecture, including datapaths, pipelining, parallelism, memory systems, and data movement.
• Familiarity with RTL design and verification.
• The ability to take an open-ended research problem, develop a hypothesis, build a prototype, and evaluate it quantitatively.
• Strong programming skills and comfort working across software, architecture, and hardware.
• The curiosity to challenge established approaches and the engineering discipline to distinguish promising ideas from impractical ones.
• Comfort using AI tools to accelerate research, development, and experimentation.

Especially useful experience

Research or practical work involving

• LLM or transformer accelerators, including attention and KV-cache optimisation.
• Binary, ternary, or other low-precision neural networks.
• Model compression, quantisation, sparsity, or inference optimisation.
• Hardware generation, compilers, XLS, or automated RTL generation.
• FPGA-based neural network acceleration.
• Compute-in-memory, near-memory computing, or hardwired neural network architectures.

A PhD is not a strict requirement. We're more interested in exceptional technical ability, original thinking, and evidence that you can turn ideas into working systems.

You'll help invent a new approach to AI inference hardware from its earliest stage.

Rather than optimising an existing processor architecture, you'll have the opportunity to rethink fundamental assumptions about how neural networks should be implemented in silicon.

You'll work directly with the founders, have meaningful ownership over architectural decisions, and see your ideas progress from research to physical chips.

We're a small team moving quickly on a technically ambitious problem. We care about independent thinking, rigorous experimentation, and building things that work.

We're currently based in San Francisco and London.