Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.
We look for infrastructure engineers who are excited to tackle unsolved problems. Progress on an LPM is gated by how fast we can evaluate it: large-scale backtesting against decades of physical observations, ensemble generation, and rollout evaluation across model scales.
Responsibilities
Your mission is to make inference so fast and cheap that evaluation never gates research.
Build high-throughput inference systems for large-scale evaluation, backtesting, and scoring against historical physical observations
Design and implement techniques that improve latency, throughput, and efficiency for real-time inference
Optimize the inference stack to fully utilize hardware FLOPs, bandwidth, and memory
Extend orchestration frameworks (e.g. Kubernetes, Ray, Slurm) for distributed inference and large-batch evaluation sweeps
Establish standards for reliability, observability, and reproducibility across the inference stack, so every evaluation is trustworthy and repeatable
Collaborate with researchers to enable high-performance inference for novel architectures as they emerge
What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
Experience building or optimizing inference and serving systems for throughput and latency (e.g. TensorRT)
Understanding of distributed compute, GPU parallelism, and hardware-aware optimization
Deep familiarity with deep learning frameworks (e.g. PyTorch, JAX) and their underlying system architectures
Strong engineering skills: performant, maintainable code and the ability to debug complex codebases
Bonus: contributions to open-source inference or systems infrastructure (e.g. vLLM, SGLang, Triton)