Member of Engineering (Inference Infrastructure)
- Role
- DevOps
- Employment
- Full-time
Open to GB only. Set where you work from to check your eligibility.
No BS summary
Infrastructure engineer for GPU workload scheduling and inference serving optimization. Needs strong Go or similar programming, systems engineering, and Kubernetes internals experience. Remote role with UK applicant requirement shown on the linked page.
Core skills
Required skills
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
What you'll do
- 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
What they require
- 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
- Preferred: experience in systems serving large scale inference requests
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
Poolside builds AI systems for software development, including agentic systems, coding assistants, and frontier models deployed into development environments of security-conscious enterprises.