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About the Role

We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, enabling AI to predict and alter future events. This role focuses on optimizing inference systems to accelerate research and development.

Responsibilities

• 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.
• Collaborate with researchers to enable high-performance inference for novel architectures.

Requirements

• 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.
• Contributions to open-source inference or systems infrastructure (e.g. vLLM, SGLang, Triton) are a plus.

Skills

• Inference systems
• Serving systems
• Throughput optimization
• Latency optimization
• Distributed compute
• GPU parallelism
• Hardware-aware optimization
• Deep learning frameworks
• PyTorch
• JAX
• System architectures
• Performant code
• Maintainable code
• Debugging complex codebases
• Kubernetes
• Ray
• Slurm
• TensorRT
• vLLM
• SGLang
• Triton

About the Company

• Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
• 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.