Causal

Causal

Member of Technical Staff — Inference Infrastructure

San Francisco · Staff+

Sponsorship not specifiedDetected 3 days ago
KubernetesDeep LearningPyTorchAI OrchestrationRoboticsProblem Solving

About the role

  • 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

  • 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
  • Collaborate with researchers to enable high-performance inference for novel architectures as they emerge
  • Experience building or optimizing inference and serving systems for throughput and latency (e.g. TensorRT)

Requirements

  • We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
  • Strong engineering skills: performant, maintainable code and the ability to debug complex codebases
  • We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Nice to have

  • contributions to open-source inference or systems infrastructure (e.g. vLLM, SGLang, Triton)

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)

Benefits

  • Deep familiarity with deep learning frameworks (e.g. PyTorch, JAX) and their underlying system architectures

Company info

  • Your mission is to make inference so fast and cheap that evaluation never gates research.
  • What we're looking for

This listing is sourced directly from Causal's careers page and normalized into a canonical job model.