AI System Architect in Oxford

AI System Architect in Oxford

Oxford Full-Time 80000 - 100000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Lead AI system architecture, bridging AI workloads with hardware and software execution.
  • Company: Join Lumai, a forward-thinking tech company focused on AI innovation.
  • Benefits: Competitive salary, share options, health insurance, and generous holiday allowance.
  • Other info: Dynamic team culture with opportunities for professional growth and development.
  • Why this job: Shape the future of AI technology and make a real impact in a collaborative environment.
  • Qualifications: Deep understanding of AI/ML workloads and experience in system architecture.

The predicted salary is between 80000 - 100000 £ per year.

We are looking for a System Architect who thinks in AI first and hardware second. You will own the architectural vision that bridges our AI workload requirements with our silicon and software execution. This is not a role for someone who designs chips and then asks what AI runs on them — it's a role for someone who deeply understands AI models, inference and training pipelines, and then works backwards to define the hardware and software systems that serve them best. You will sit at the intersection of leadership, hardware engineering, and software engineering — translating high-level product strategy into concrete architectural decisions and ensuring all three teams are aligned, unblocked, and pulling in the same direction.

What You'll Do

  • Architecture & Technical Leadership
    • Define and own the end-to-end system architecture, from AI workload characterisation through to chip microarchitecture trade-offs and software stack interfaces.
    • Drive architectural decisions by starting with AI model and operator analysis — identifying compute, memory bandwidth, data movement, and sparsity patterns that constrain and shape the hardware design.
    • Develop and maintain architectural specifications, performance models, and design documents that serve as the single source of truth across teams.
    • Evaluate architectural trade-offs (latency vs. throughput, on-chip vs. off-chip memory, dataflow strategies, precision formats) with a quantitative, workload-grounded methodology.
    • Stay current with the frontier of AI research (model architectures, training techniques, inference optimisations) and translate emerging trends into architectural foresight.
  • Cross-functional Coordination
    • Act as the primary technical bridge between the leadership team, hardware engineering, and software engineering — ensuring that decisions made in one domain are properly communicated, challenged, and integrated in others.
    • Partner with the leadership team to translate business goals and product roadmap into architectural requirements and phased execution plans.
    • Work closely with the hardware team to ensure microarchitectural decisions are grounded in realistic AI workload demands; push back constructively when hardware-centric thinking diverges from AI requirements.
    • Collaborate with the software team to define clean hardware/software interfaces, programming models, and runtime abstractions that make the hardware genuinely usable for AI practitioners.
    • Facilitate architectural reviews and design discussions that create shared understanding rather than siloed decision-making.
  • Execution & Delivery
    • Identify and resolve cross-team dependencies and ambiguities early, before they become schedule risks.
    • Define and track key architectural metrics and milestones throughout the chip development lifecycle (pre-RTL through tape-out and post-silicon validation).
    • Support benchmarking and performance analysis efforts, helping teams understand where the system delivers against AI workload targets and where it falls short.

What We're Looking For

  • Must-Have
    • Deep, hands‑on understanding of modern AI/ML workloads — transformer architectures, convolutional networks, recommendation systems, or similar — including their compute and memory access patterns.
    • Proven experience defining system or chip architecture in the context of AI/ML acceleration (inference, training, or both).
    • Ability to build and use analytical performance models (roofline models, memory bandwidth analysis, cycle‑accurate estimates) to guide architectural decisions.
    • Strong communication skills with the ability to adapt technical depth for leadership, hardware engineers, and software engineers alike.
    • Experience working across hardware and software boundaries — comfortable discussing ISA design, compiler interfaces, and runtime scheduling as well as datapath microarchitecture.
    • Suitable University education and/or practical experience.
  • Strong Preference For
    • Experience at an AI chip startup, AI hardware team at a major tech company (e.g. Google TPU, Meta MTIA, AWS Trainium/Inferentia, Tesla Dojo), or a leading fabless semiconductor company.
    • Familiarity with AI compiler stacks (MLIR, XLA, TVM, Triton) and how they interact with hardware architecture decisions.
    • Understanding of chip development processes: RTL design, physical design constraints, and post‑silicon bring‑up.
    • Experience with or exposure to on‑device / edge inference as well as datacenter‑scale deployments.
    • Track record of influencing architectural direction through written specs and data‑driven arguments rather than authority alone.

What Success Looks Like

In your first 90 days, you will have audited the current architectural approach against a defined set of target AI workloads, identified the top architectural risks to schedule and performance, and established a working rhythm with the HW and SW leads. Within six months, you will be the person who others come to when a cross‑team decision needs an owner — and the one who keeps the architecture honest against the AI workloads we are building for.

Compensation & Benefits

  • Highly Competitive Salary: We are not saying our salary is a blank check, but let's just say it won't be a source of your stress.
  • Share Option Scheme: We are all in this together! We believe in shared success while we build the Lumai of tomorrow.
  • Pension Scheme: Plan for retirement with AVIVA.
  • Private Health Insurance: We firmly believe that you come first, and a happy you is a healthy you! Look after yourself and your loved ones with AXA.
  • Cycle to Work: Spread the cost of a bike, a bike and accessories or just accessories and save on tax.
  • L&D Allowance: Stay at the forefront of your field with a £500 annual development budget.
  • Subsidised On‑site Lunches: Enjoy on‑site healthy meals at half the price, as Lumai covers 50% of the cost.
  • Holidays: Enjoy some deserved "me time" with 25 days paid holiday (plus bank holidays) per year.
  • Socials: Be part of an inclusive community enjoying occasional all‑company off‑sites, lunches and socials.

Lumai is an equal opportunity employer. We make hiring decisions on merit, scope‑fit, and the strength of the working relationship we expect to build with each hire. Applications welcome from candidates of any background. If you are not sure whether you are a fit, send a note anyway.

AI System Architect in Oxford employer: Lumai

At Lumai, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As an AI System Architect, you will thrive in an environment that prioritises employee growth through continuous learning opportunities, competitive compensation, and a strong emphasis on work-life balance. Our commitment to inclusivity and shared success, combined with our prime location, makes Lumai a truly rewarding place to advance your career in cutting-edge AI technology.

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Contact Details:

Lumai Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land AI System Architect in Oxford

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.

Tip Number 2

Show off your skills! Create a portfolio or GitHub repository showcasing your AI projects and architectural designs. This gives potential employers a taste of what you can do and sets you apart from the crowd.

Tip Number 3

Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical teams.

Tip Number 4

Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.

We think you need these skills to ace AI System Architect in Oxford

AI/ML Workload Understanding
System Architecture Definition
Analytical Performance Modelling
Communication Skills
Cross-Functional Coordination
Architectural Specification Development
Hardware/Software Interface Design

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter for the AI System Architect role. Highlight your experience with AI workloads and architectural decisions, showing us how you fit into our vision.

Showcase Your Skills:Don’t just list your skills; demonstrate them! Use specific examples from your past work that illustrate your hands-on understanding of AI/ML workloads and system architecture.

Be Clear and Concise:Keep your application clear and to the point. We appreciate straightforward communication, so make sure your key achievements and experiences stand out without unnecessary fluff.

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role as quickly as possible!

How to prepare for a job interview at Lumai

Know Your AI Inside Out

Make sure you have a deep understanding of modern AI/ML workloads, especially transformer architectures and convolutional networks. Be ready to discuss their compute and memory access patterns in detail, as this will show your expertise and help you connect with the interviewers.

Bridge the Gap

Prepare to demonstrate how you can act as a technical bridge between hardware and software teams. Think about examples from your past experiences where you successfully facilitated communication and collaboration across different domains, ensuring everyone was aligned on architectural decisions.

Quantitative Thinking is Key

Brush up on your analytical skills! Be prepared to discuss how you would use performance models to guide architectural decisions. Having concrete examples of how you've applied these models in previous roles will give you an edge.

Stay Current with AI Trends

Familiarise yourself with the latest trends in AI research and be ready to discuss how they could influence architectural decisions. Showing that you’re not just knowledgeable but also forward-thinking will impress the interviewers and demonstrate your passion for the field.