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Engineering Manager, ML Infrastructure, London
AppleLondon, England, United Kingdom
Apply Apple's cloud AI inference platform is growing quickly, and so is the organisation that builds it. We have a complex inference stack, a rapidly changing generative-AI landscape, and more responsibility than our current teams can hold — so we are looking for engineering managers to take ownership of components of that stack and lead the teams that build them. This role is based in London.
Private Cloud Compute is the system that lets Apple Intelligence reach beyond the device without compromising a user's privacy: generative AI inference running in Apple's cloud, with verifiable privacy guarantees no other large-scale AI platform offers. It is the server software behind Apple Intelligence, and it is the critical function this organisation exists to deliver.
The stack is deep. On-device client frameworks hand requests to a cloud service that attests, routes, and orchestrates them; an inference engine serves them; and model runtimes execute across heterogeneous hardware platforms, from Apple silicon to industry-standard accelerators, each with different performance characteristics and constraints. Cutting across all of it are the problems that decide whether the platform is fast, affordable, and operable: context and cache management, model asset management and lifecycle, throughput and latency, observability, and the developer and test infrastructure that everything else is built on.
You would own set of components in this stack. The generative-AI landscape is a rapidly evolving and we are looking for managers with an agile mindset that are energized by change. You can hold a clear technical direction while the ground shifts and have a strong desire to help define our roadmap.
Day to day you will hire, grow, and lead a team of engineers; own delivery against a roadmap you help set; lead design reviews and make architectural calls yourself when your team needs a decision; run a healthy on-call and incident practice; and partner across time zones with ML research, hardware and platform teams, security and privacy, SRE, and the product teams that depend on you. You will work with teams in London, Cupertino, and Seattle whose work spans low-level operating systems and accelerator runtimes through data-centre services, network protocols, and public APIs.
You should be technically credible — you do not need to be the strongest individual contributor on the team, but you must be able to hold your own in a design review, read the code when it matters, and tell a good argument from a confident one. You should be able to absorb shifting priorities on behalf of your team rather than passing them along. And you should care about the privacy promise this platform makes to users; much of what makes the engineering here hard, and interesting, is that the usual shortcuts are not available to us.
- Build, coach, and retain a high-performing, inclusive team: hiring, onboarding, growth, feedback, and performance management.
- Take ownership of one or more areas of the inference stack, and be accountable for their delivery, quality, and technical direction.
- Set and defend a technical roadmap that stays credible as priorities and platforms change, balancing near-term delivery against longer-term investment.
- Partner across engineering, research, hardware, security and privacy, SRE, and product to deliver outcomes that span team boundaries.
- Hold a rigorous engineering bar: honest measurement, reproducible results, operational readiness, incident review, and documentation.
- Represent your team's work to senior leadership, and advocate for the resources and direction it needs.
- Develop technical leads within your team, delegating real architectural ownership rather than retaining it.
- Ensure the team applies privacy-by-design and secure-by-design principles throughout, particularly around what may and may not be observed or logged in a system handling user content.
- Experience managing software engineers, including hiring, coaching, feedback, and performance management.
- A strong software engineering background in systems, backend, distributed systems, or platform work, with the ability to engage deeply and specifically in design trade-offs.
- Demonstrated ownership of delivery on an infrastructure or platform team: roadmap, sequencing, cross-team dependencies, and shipped results.
- An agile mindset and a track record of operating effectively in ambiguity — able to absorb rapidly shifting priorities without losing execution discipline or the team's trust.
- Excellent written communication, and effective working habits across geographies and time zones. UK/US collaboration particularly relevant.
- A genuine security and privacy mindset for systems handling sensitive user content.
- Any strong combination of the following is interesting to us — we do not expect all of them:
- Experience with LLM inference or model serving at scale: batching and scheduling, KV-cache reuse, paged attention, prefix caching, disaggregated serving, speculative decoding, quantisation, or model parallelism.
- Experience with GPU or custom-accelerator performance work, and with the internals of an ML runtime or framework.
- Experience leading teams that own a platform other engineers build on, including API and compatibility stewardship across versions and hardware generations.
- Familiarity with production operations for latency-sensitive services: SLOs and error budgets, observability, capacity planning, canary and rollback discipline.
- Background in developer experience and build or test infrastructure, and a view on how to reduce cycle time without lowering quality.
- Working knowledge of Swift; systems-language experience (C++, Rust, Go) and Python tooling experience are all valuable here.
- Experience with privacy-preserving, security-sensitive, or attested systems, and with reasoning rigorously about what may be logged or measured.
- Experience growing a team from a small senior core, and developing engineers into technical leadership.