Causal Labs — Machine Learning Infrastructure Engineer

davidjoseph-co; San Francisco (CA); On-site; USD 200,000 - 400,000; Full time

Benefits offered by this job

Relocation support

Visa sponsorship

On-site in SF

Equity compensation

Job summary

Unknown Company in San Francisco, CA seeks a lead ML infrastructure engineer to own the distributed training and inference backbone for a foundation model trained from scratch. You will stand up clusters, build data pipelines at petabyte scale, and optimize GPU performance across model scales.

You will work with FSDP/DeepSpeed, NVIDIA GPUs, Linux, Python and C++, in a distributed cloud environment across GCP/AWS/Azure, with relocation supported.

Qualifications

• 2–10 years building large-scale ML infrastructure for core foundation models.
• Hands-on experience building infrastructure for foundation models trained from scratch.
• Background at science-focused or physical-AI company (e.g., robotics, biology).
• Deep, demonstrable expertise optimizing large-scale training and inference workloads.
• Experience with distributed training frameworks such as FSDP or DeepSpeed.
• Able to relocate and work on-site in San Francisco 5 days/week.

Responsibilities

• Design, deploy, and maintain large distributed ML training and inference clusters.
• Build scalable end-to-end pipelines for petabyte-scale datasets and training.
• Research training approaches and parallelization techniques.
• Profile and debug low-level GPU operations to optimize performance.
• Stay updated with new research and bring innovative ideas.

Skills

Distributed training

GPU optimization

Python

C++

Cloud platforms

Tools

FSDP

DeepSpeed

NVIDIA GPUs

Linux

Kubernetes

Docker

GCP

AWS

Azure

Job description

San Francisco, CA · On-site (5 days/week) · Full-timeCompensation: $200K–$400K + competitive early-stage equity

About the Company

Our client is a Series A AI research lab building large-scale foundation models for scientific and physical-AI domains. Backed by top-tier investors, they are pursuing a deliberately non-consensus technical thesis and are among the best-funded teams in their space. The founding team comes from self-driving, robotics, and scientific research, and they are scaling their research and engineering org significantly this year.

Founded 2024 · Small, fast-growing team · Industry: AI / foundation models / physical AI

The Role

You would own the distributed training and inference backbone for a foundation model trained from scratch — standing up clusters, building data and training pipelines at petabyte scale, and squeezing performance out of GPUs at a low level across model scales.

What you'll be doing

• Design, deploy, and maintain large distributed ML training and inference clusters
• Build efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and training across the full ML lifecycle
• Research and test training approaches, including parallelization techniques and numerical-precision trade-offs across model scales
• Profile and debug low-level GPU operations to optimize performance
• Track new research and bring fresh ideas into the work

Tech stack: Distributed training frameworks (FSDP, DeepSpeed), NVIDIA GPUs, Linux, Python, C++, Kubernetes/Docker, and a major cloud platform (GCP, AWS, or Azure).

Requirements

• 2–10 years building large-scale ML infrastructure for core foundation models
• Hands-on experience building infrastructure for foundation models trained from scratch, rather than fine-tuning existing models
• A background at a science-focused or physical-AI company (for example self-driving, robotics, or biology)
• Deep, demonstrable expertise optimizing large-scale training and inference workloads
• Working proficiency with distributed training frameworks such as FSDP or DeepSpeed
• A clear pattern of intentional, mission-driven career decisions
• Able to work on-site 5 days/week in San Francisco (relocation supported)

Nice to Haves

• Generalist experience spanning the full ML lifecycle
• Low-level GPU performance optimization and debugging (CUDA, JAX)

Why Join

• Take a bet on a distinctive, non-consensus approach to building intelligence
• Join early, with real ownership of the training and inference backbone
• Work in a domain with fast, objective ground-truth feedback and data at a scale beyond typical LLM training
• Well-funded and building a strong, senior research and engineering team

Details

• Location: San Francisco, CA
• Work policy: In-person 5 days/week (relocation supported)
• Compensation: $200K–$400K + competitive early-stage equity
• Visa sponsorship: Open to supporting work authorization for the right candidate
• Employment type: Full-time