Member of Technical Staff — Inference Infrastructure
Causal Labs· Infrastructure
San Francisco, SingaporeFull-timePosted 2mo ago
About the role
Mission to build fast, scalable inference infrastructure for large-scale evaluation of a Large Physics foundation Model (LPM), enabling rapid backtesting, 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
- 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
Qualifications
- 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)
Skills
Required:Inference system designDistributed compute and GPU optimizationDeep learning frameworks
Last updated Oct 2, 2026. Interested? Apply on the company site.
About Causal Labs
Towards general causal intelligence
Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather
causalityphysicsweatherAIfoundation model
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