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Member of Technical Staff — Inference Infrastructure

Causal Labs

San Francisco, CAJobPosted 2mo agoStill listed 2 days ago

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At a glance

Compensation
No compensation found
Location
San Francisco, CA
Work Authorization
Not specified

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Job overview

Causal Labs is hiring a Member of Technical Staff — Inference Infrastructure. Causal Labs is building a Large Physics Foundation Model (LPM) to achieve general causal intelligence, capable of predicting the future and identifying actions to alter it. The company focuses on physical systems, starting with weather, to understand causality. The founding team has experience deploying AI in robotics, drug discovery, and particle physics at leading institutions.

Key focus areas include 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, and Optimize the inference stack to fully utilize hardware FLOPs, bandwidth, and memory.

Successful candidates bring Experience Building Or Optimizing Inference And Serving Systems For Throughput And Latency, Understanding Of Distributed Compute GPU Parallelism And Hardware‑Aware Optimization, and Deep Familiarity With Deep Learning Frameworks And Their Underlying System Architectures. Important skills include Building High-Throughput Inference Systems, Designing Techniques To Improve Latency, Optimizing Inference Stack, Extending Orchestration Frameworks, Establishing Standards For Reliability, and Observability. Preferred (not required): Kubernetes, Ray, Slurm, and TensorRT.

Skills & qualifications

RequiredNice to have

Skills

Building High-Throughput Inference SystemsDesigning Techniques to Improve LatencyOptimizing Inference StackExtending Orchestration FrameworksEstablishing Standards for ReliabilityObservabilityReproducibilityCollaborating With ResearchersRelentless Problem-SolvingRapid ExecutionQuick Learning in Unfamiliar DomainsBuilding or Optimizing Inference and Serving SystemsUnderstanding of Distributed ComputeGPU ParallelismHardware-Aware OptimizationDeep Learning FrameworksSystem ArchitecturesPerformant CodeMaintainable CodeDebugging Complex CodebasesKubernetesRaySlurmTensorRTPyTorchJAXvLLMSGLangTritonContributions to Open-Source Inference or Systems Infrastructure

Qualifications

Experience Building or Optimizing Inference and Serving Systems for Throughput and LatencyUnderstanding of Distributed Compute GPU Parallelism and Hardware‑Aware OptimizationDeep Familiarity With Deep Learning Frameworks and Their Underlying System ArchitecturesStrong Engineering Skills Performant Maintainable Code and Ability to Debug Complex Codebases

Full job description

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)

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