Summary
✨ AI‑Generated
Join a high-performance AI engineering organization building large-scale inference infrastructure for advanced models. You will work on demanding systems problems spanning infrastructure, deployment, performance, and reliability, collaborating closely with engineers and researchers to push the boundaries of AI-powered software development.
Highlights
Opportunity to work on ambitious AI infrastructure at significant scale, collaborate with highly capable engineering and research talent, and contribute to advancing software development through agentic AI.
Description
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 utilizationBuild API and tooling to help manage the lifecycle of GPU workloads and troubleshoot failuresDesign and improve inference control plane to speed up model deployment and inference request servingCollaborate with research to improve research velocity continuously
Skills & Experience
Strong programming skills in Go, or other similar languagesStrong 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 issuesPlus: experience in systems serving large scale inference requests
PROCESS
Intro call with a member of our teamTechnical Interview(s) with one of our Members of EngineeringTeam fit call with the People teamFinal interview with one of our Founding Engineers
Benefits
Fully remote work & flexible hours37 days/year of vacation & holidaysHealth insurance allowance for you & dependents16 weeks of flexible, full-pay parental leaveWell-being, always-be-learning & home office allowancesCompany-provided equipmentFrequent team get togethersDiverse & inclusive people-first cultureDiverse & inclusive people-first culture