Inference System & Performance Engineer - Member of Technical Staff in City of Westminster

Inference System & Performance Engineer - Member of Technical Staff in City of Westminster

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

  • Tasks: Design and optimise cutting-edge inference systems across diverse hardware environments.
  • Company: Join Callosum, the Intelligent Systems Company, pioneering the future of AI.
  • Benefits: Competitive salary, equity, private healthcare, and relocation support.
  • Other info: Inclusive workplace with opportunities for personal and professional growth.
  • Why this job: Be at the forefront of AI innovation and tackle complex system challenges.
  • Qualifications: Strong background in systems engineering and experience with GPU workloads.

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

About Us

The last era of AI scaled on a single bet: bigger models, more identical chips, more data. As problems grow more complex and the requirements of intelligence more diverse, that bet is breaking down. Real-world problems are heterogeneous: no single model or chip can solve them alone. The next era of AI requires heterogeneity at the infrastructure level - diverse models on diverse chips, each with distinct strengths, co-evolving into systems of capability that move the Pareto frontier of what is possible. That's what we are building. Callosum is the Intelligent Systems Company. We started from questioning what actually creates intelligence. We believe there is no single answer, but rather a system-level solution. We co-evolve models, workflows, and silicon together to show that intelligence does not come from a single component, but it emerges from the diversity of co-optimised mechanisms working together and aware of each other. Heterogeneity will define the next era of compute, and is a principle that holds in biological, neuronal, and economic systems alike. In early 2026 we launched with results showing orders of magnitude improvements in performance, and this is only the beginning. Agentic AI is the future of how intelligence is deployed: multi-step, long-horizon, and operating in changing environments. These systems are inherently heterogeneous, and can only be as powerful as the infrastructure that runs them. We are engineers and scientists based in London, working together across the full depth of the stack. We are curious, intellectually honest, and building what doesn't exist yet. If you thrive on uncharted territory and are energised by the scale of the challenge, we'd love to hear from you.

About the Role

Standard inference architectures typically focus on monolithic chip types and model classes. Callosum intentionally breaks this mold, operating heterogeneous hardware at scale across a diverse model portfolio. Success in this environment requires an inference layer built entirely from first principles. Sitting at the heart of our technical mission, this position owns end-to-end performance for our inference platforms. Your focus will span KV cache strategies, batching internals, memory management, and multi-node scheduling. You will develop the core software driving execution speed, silicon efficiency, and platform scalability as we expand our hardware and model footprint. This is a high-leverage role tackling complex system challenges across the entire stack.

What You’ll Build

  • Design and optimise inference serving systems across heterogeneous multi-GPU and multi-node environments
  • Own KV cache lifecycle management, batching strategies, and memory allocation to maximise throughput and minimise latency
  • Profile and tune GPU kernels, identify bottlenecks across compute, memory, and network, and implement targeted optimisations
  • Build and improve scheduling logic for continuous batching, disaggregated prefill/decode, and speculative decoding
  • Work with networking primitives - NCCL, NVLink, RDMA, InfiniBand, RoCE - to optimise communication across distributed inference workloads
  • Develop tooling for performance visibility, regression detection, and benchmarking across hardware configurations

What you Bring

  • Deep understanding of LLM inference internals: KV cache lifecycle, memory management, attention mechanisms, and serving architectures
  • Strong systems engineering background with proven experience optimising distributed GPU workloads
  • Proficiency in C++, CUDA, Python, Rust, or similar - and the instinct to go low-level when it matters
  • Hands‑on debugging skills across GPU, networking, and Linux systems - able to work from first principles with limited tooling

What Sets You Apart

  • Experience building or significantly optimising production‑grade, high‑throughput model serving stacks
  • Multi‑GPU and multi‑node inference optimisation using NCCL, NVLink, RDMA, InfiniBand, or RoCE
  • GPU memory profiling, CUDA or Triton kernel optimisation
  • Linux performance analysis and optimisation

What We Offer

  • Competitive Salary, determined by skills and experience
  • Equity & Ownership
  • Private healthcare
  • We offer Visa sponsorship and relocation benefits to hire the best in the world
  • We work in person at our London office. You'll have the tools, space and setup to do your best work, and if you have specific needs, just tell us

We're committed to building an inclusive workplace where everyone feels welcome, and believe in equal opportunities for all.

Inference System & Performance Engineer - Member of Technical Staff in City of Westminster employer: jobr.pro

At Callosum, we are not just building AI; we are redefining its future through a collaborative and innovative work culture in the heart of London. Our commitment to diversity in technology fosters an environment where engineers and scientists thrive on tackling complex challenges together, with ample opportunities for personal and professional growth. With competitive salaries, equity options, and a focus on inclusivity, we ensure that every team member feels valued and empowered to contribute to groundbreaking advancements in intelligent systems.

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Contact Details:

jobr.pro Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Inference System & Performance Engineer - Member of Technical Staff in City of Westminster

Join Local Tech Meetups

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Contribute to Open Source Projects

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We think you need these skills to ace Inference System & Performance Engineer - Member of Technical Staff in City of Westminster

Inference Layer Development
KV Cache Lifecycle Management
Batching Strategies
Memory Management
Multi-Node Scheduling
GPU Kernel Profiling and Tuning
Distributed GPU Workload Optimisation

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at jobr.pro.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at jobr.pro and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at jobr.pro

Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If jobr.pro uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.