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

What you'll own

• Research and develop novel accelerator architectures for LLM inference, focusing on binary and ternary weights, model-specific computation, and efficient data movement.

• Explore how model weights and operations can be encoded directly into silicon, and how much hardware can be reused across different models.

• Investigate trade-offs across model quality, inference latency, throughput, silicon area, and power consumption.

• Design and evaluate compute architectures, memory hierarchies, attention mechanisms, KV-cache systems, and low-precision arithmetic.

• Explore automated approaches to translating model architectures and trained weights into efficient hardware, including compiler-driven RTL generation.

• Build architectural simulators, performance models, and FPGA prototypes to validate ideas before committing to silicon.

• Work closely with our founders and ASIC engineers to turn promising research into implementable microarchitectures.

• Help define the longer-term technical direction of Lamb Labs' inference hardware.

What we're looking for

• 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.