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.
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.
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.
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.
Follow the latest research on LLMs, inference and source code generation
Propose and evaluate innovations, both in the quality and the efficiency of the inference
Monitor and implement LLM inference metrics in production
Write high-quality high-performance Python, Cython, C/C++, Triton, ThunderKittens, native CUDA, Amazon Neuron code
Work in the team: plan future steps, discuss, and always stay in touch
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
Intro call with one of our Founding Engineers
Technical Interview(s) with one of our Founding Engineers
Team fit call with the People team
Final interview with one of our Founding Engineers
Fully remote work & flexible hours
37 days/year of vacation & holidays
Health insurance allowance for you & dependents
Company-provided equipment
Well-being, always-be-learning & home office allowances
Frequent team get togethers
Diverse & inclusive people-first culture