Member of Engineering (Inference Infrastructure)

Poolside is hiring a Member of Engineering (Inference Infrastructure) for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Poolside.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
This is listed as a remote role.
The posting mentions GPU, gpu scheduling and inference optimization among its tools and skills.
Poolside has 1 other open role on Level.

AI in this role

Compute engineer focused on GPU workload scheduling and inference serving optimization for large language models.

gpu

gpu-schedulinginference-optimizationdistributed-systemsinfrastructure

ABOUT POOLSIDE

In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.

Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.

ABOUT OUR TEAM

We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.

Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.

ABOUT THE ROLE

You’ll be working in the compute team focusing on GPU workload scheduling and inference serving optimization. You would partner with the inference team to improve our inference throughput and latency for evals and reinforcement learning. You would collaborate with our scalability team to focus on stabilizing our large scale fault tolerant training. You would also be in close contact with the infra team to make sure our GPU nodes are all healthy and fully utilized.

We are one of the key teams to improve the research velocity. Any improvement on our systems has a wide impact on researchers and can contribute to the poolside mission on building a frontier model.

YOUR MISSION

To optimize GPU utilization across the company and to deliver stable and scalable inference serving stack for Poolside’s researchers.

RESPONSIBILITIES

Design and develop internal scheduling system to maximize GPU utilization
Build API and tooling to help manage the lifecycle of GPU workloads and troubleshoot failures
Design and improve inference control plane to speed up model deployment and inference request serving
Collaborate with research to improve research velocity continuously

SKILLS & EXPERIENCE

Strong programming skills in Go, or other similar languages
Strong systems engineering background: distributed systems, schedulers, control planes, or high-throughput data planes.
Production experience with Kubernetes internals — controllers, informers, operators — not just deploying to it.
Bias toward observability and debuggability: building a system that is easy to navigate when debugging production issues
Plus: experience in systems serving large scale inference requests

PROCESS

Intro call with a member of our team
Technical Interview(s) with one of our Members of Engineering
Team fit call with the People team
Final interview with one of our Founding Engineers

BENEFITS

Fully remote work & flexible hours
37 days/year of vacation & holidays
Health insurance allowance for you & dependents
16 weeks of flexible, full-pay parental leave
Well-being, always-be-learning & home office allowances
Company-provided equipment
Frequent team get togethers
Diverse & inclusive people-first culture
Diverse & inclusive people-first culture

How we rate this

Member of Engineering (Inference Infrastructure) at Poolside rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Member of Engineering (Inference Infrastructure) is an open job at Poolside.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Remote (EMEA) and is marked remote.
The job was posted on July 15, 2026.
Applications are handled by Poolside. Apply on Poolside

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

GPU SchedulingInference OptimizationDistributed SystemsInfrastructureGPU

Questions you could be asked

Tell me about a project where gpu scheduling was part of your work. What did you do?
Tell me about a project where inference optimization was part of your work. What did you do?
Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where infrastructure was part of your work. What did you do?
Walk me through how you've used GPU in your day-to-day work.

Adapt your resume

List these exact terms on your resume: GPU Scheduling, Inference Optimization, Distributed Systems, Infrastructure, and GPU. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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