ML Systems Engineer, Inference Benchmarking

CommonAI C.I.C.; Cambridge; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Pension

Professional development opportunities

Job summary

CommonAI CIC invites engineers to join the Scaling Inference Lab in Cambridge to build reproducible LLM serving workloads and benchmarks. You will connect application behavior to physical measurements and the lab’s simulation platform, focusing on long-context inference and agentic workloads.

You will work with measurement and simulator engineers to preserve workload behavior across real systems and models, and document configurations, provenance and usage for reproducibility.

Qualifications

• Experience building or evaluating ML systems, inference services or comparable workload-driven systems.
• Strong software skills for constructing reliable benchmark tools and debugging interactions.
• Understanding of LLM inference and how serving choices affect performance and output quality.
• Experience with AI coding agents and validating generated code or data transformations.

Responsibilities

• Build reproducible LLM serving workloads and benchmark tools, including long-context workloads.
• Characterise request patterns, lengths, caching, batching, and tool interactions.
• Define performance measures alongside application-quality requirements.
• Collaborate with measurement and simulator engineers to reproduce workloads on physical systems.
• Investigate how workload changes affect conclusions and keep benchmarks relevant.

Skills

ML systems experience

Benchmark tooling

LLM inference

Agentic workloads

Code validation

Tools

vLLM

SGLang

Job description

About CommonAI

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing.

Opportunity

Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes.

What you will do

• Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software.


• Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests.


• Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions.


• Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter.


• Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve.


• Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work.


• An example early project would be to build a reproducible agentic workload and work with measurement colleagues to compare its performance across two systems, under agreed application-quality and service requirements.


• Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems.


• Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure.


• Understanding of LLM inference and an interest in how serving choices affect performance and output quality.


• Care in designing reproducible comparisons and interpreting results against stated application requirements.


• Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations.

Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions.

Why join us

• Help build UK AI capability and improve how investment and compute serve the public good.


• Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities.


• Shape a growing team with startup pace and close academic links.


• Use frontier models and coding agents with a substantial tooling budget.

A collaborative and supportive work environment The opportunity to have a high impact in a growing organisation Competitive salary package, including pension and benefits Professional development opportunities Networking opportunities with influential figures from across the tech sector and academia A vibrant office environment located a few minutes' walk away from Cambridge train station

Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome.

We are open to permanent employment, part-time arrangements and consultancy.

We would like you to start as soon as practical and can be flexible for the right person.

We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability.

Relevant postgraduate study is useful, but qualifications are secondary to experience and the q