
We build the models. You build the future. AGI for the enterprise, starting with software agents.

We build the models. You build the future. AGI for the enterprise, starting with software agents.
Core offering: Enterprise foundation models and agentic systems for software engineering
Deployment: On‑prem / VPC / workstation deployable with enterprise governance
Founders / leadership: Jason Warner (CEO) and Eiso Kant (CTO / co‑founder)
Funding: $626M total reported (includes $500M Series B)
Employees: Approx. 330
Automating and augmenting software engineering workflows with foundation models and software agents.
Enterprise AI / Developer tools
$126M
Reported large seed raised prior to 2024
$500M
Reported Series B announced Oct 2, 2024
“Participation from strategic corporate and institutional investors including Nvidia and eBay Ventures reported alongside institutional backers”
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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 will be focused on building out our multi-device inference of Large Language Models, both standard transformers and custom linear attention architectures. You will be working with lowered precision inference and tensor parallelism. You will be comfortable diving into vLLM, Torch, AWS libraries. You will be working on improvements for both NVIDIA and AWS hardware. You will be working on the bleeding edge of what's possible and will find yourself, hacking and testing the latest vendor solutions. We are rewrite-in-Rust-friendly.
YOUR MISSION To develop and continuously improve the inference of LLMs for source code generation, optimizing for the lowest latency, the highest throughput, and the best hardware utilization.
Responsibilities
Skills & Experience
PROCESS
Benefits
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Experience with Large Language Models (LLM)
Confident knowledge of the computational properties of transformers
Knowledge/Experience with cutting-edge inference tricks
Knowledge/Experience of distributed and lower precision inference
Knowledge of deep learning fundamentals
Strong engineering background
Theoretical computer science knowledge is a must
Experience with programming for hardware accelerators
SIMD algorithms
Expert in matrix multiplication bottlenecks
Know hardware operation latencies by heart
Research experience
Nice to have but not required: Author of scientific papers on any of the topics: applied deep learning, LLMs, source code generation, etc
Can freely discuss the latest papers and descend to fine details
You have strong opinions, weakly held
Programming experience
Linux
Git
Python with PyTorch or Jax
C/C++, CUDA, Triton, ThunderKittens
Use modern tools and are always looking to improve
Opinionated but reasonable, practical, and not afraid to ignore best practices
Strong critical thinking and ability to question code quality policies when applicable
Prior experience in non-ML programming is a nice to have