Director of Engineering

A heavily funded AI hardware company is building a vertically integrated platform for frontier-model inference, spanning custom silicon, rack-scale systems, interconnects, compilers, runtimes and serving software.

Its first production silicon has returned and the team is now validating rack-scale systems with major AI customers. The platform targets demanding workloads including large mixture-of-experts models, long-context inference and agentic applications, with the goal of substantially improving throughput, latency, power efficiency and cost per token.

The Role

The company is hiring a Director of Engineering to build and lead the organisation responsible for turning its custom accelerator into a production-grade inference platform.

You will own the technical direction and delivery of the inference stack across model enablement, distributed execution, runtime scheduling, kernel performance and serving infrastructure. This is a hands-on leadership position requiring someone capable of building an exceptional team while remaining closely involved in architecture and performance decisions.

Responsibilities

• Define the architecture and roadmap for the end-to-end inference stack.
• Build and lead teams across inference, runtimes, kernels and distributed systems.
• Optimise prefill and decode performance across throughput, latency, memory utilisation, power and cost.
• Develop parallelism and scheduling strategies across chips, servers and racks.
• Guide work across continuous batching, KV-cache management, speculative decoding and prefill/decode disaggregation.
• Lead enablement and optimisation of transformer and MoE models.
• Partner with compiler and kernel teams on graph optimisation, operator fusion, tiling and code generation.
• Translate production workloads into improvements across compute, memory and interconnect architecture.
• Establish benchmarking, profiling, observability and performance-regression infrastructure.
• Work directly with frontier AI companies, hyperscalers and cloud customers.

Requirements

• Significant experience building high-performance inference or distributed AI systems.
• Leadership experience across inference, ML systems, accelerator software or performance engineering.
• Strong understanding of transformer inference, attention, KV-cache behaviour, batching and autoregressive decoding.
• Experience scaling large models across multiple GPUs or AI accelerators.
• Knowledge of tensor, pipeline and expert parallelism.
• Experience with vLLM, SGLang, TensorRT-LLM, PyTorch, JAX, XLA or similar systems.
• Understanding of accelerator architecture, memory hierarchies, interconnects and compute utilisation.
• Technical strength in C++, Python and CUDA, Triton, ROCm or another low-level accelerator environment.
• Ability to diagnose performance across frameworks, compilers, runtimes, kernels and hardware.
• A track record of recruiting and leading highly capable engineering teams.

Particularly Relevant Experience

• Leading inference or ML-systems teams at a frontier AI lab, hyperscaler, GPU company or accelerator startup.
• Bringing up models on new silicon before the surrounding software ecosystem was mature.
• Developing kernels for attention, GEMM, MoE, communication or quantised inference.
• Building production systems for low-latency or high-throughput LLM serving.

This is an opportunity to define the inference organisation around a new computing platform rather than inherit an established stack. You will influence both the software and silicon roadmap while helping move a technically ambitious architecture into large-scale customer deployment.

A highly competitive compensation and equity package is available.