Member of Technical Staff — Inference Infrastructure

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.

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

• 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)