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Head of Infrastructure, reporting to the VP Engineering. The customers in flight want production inference economics from the same silicon they are training on.
A Series A AI chip startup. The firm designs dedicated chips for both training and inference. Around 95 staff, around $140m raised. Engineering-heavy; the founders are ex-tier-one silicon and ex-frontier-lab. First silicon taped out earlier in the year; in customer engagement with three frontier labs and two cloud providers.
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Head of Infrastructure, reporting to the VP Engineering. Owns the firm's compute stack end-to-end — the hardware/software interface, the training cluster orchestration, the inference deployment pipeline, and the developer tooling that wraps all of it.
Direct line management of around 25 infrastructure engineers split across three squads (cluster, runtime, developer experience). Recruiting two further senior ICs in the first 6 months.
The 12-month remit is to lift the cluster from internal pilot to first external customer workloads, harden the inference pipeline against the firm's three named-customer SLAs, and ship a developer experience that makes the silicon usable to teams who have only ever known GPUs.
London-based. Office-first culture given the hardware nature of the work; structured travel to the firm's silicon partners in Taiwan and to US customers expected.
The customers in flight want production inference economics from the same silicon they are training on. One infrastructure leader owns both stacks end to end — training and inference on the same roadmap — and runs throughput, latency and unit cost hands-on across both. The work is taking the firm's silicon through to the production milestones already committed to customers.
— Peter Wood, Partner & Chief Strategy Officer
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