Causal Labs — Machine Learning Infrastructure Engineer

• Counterintuitive technical thesis: A bet that scaling LLM/video models to $1T of compute will not get us to super-intelligence. For candidates who want to bet on a non-consensus approach to intelligence.
• Best-funded Series A in physics models: Positioned as the single most well-funded Series A company building physics foundation models.
• A domain with real ground truth: Weather offers rapid, objective feedback and data volume beyond LLM-scale — a rare setting where infra work is measured against reality fast.
• Early, high-ownership seat: ~10 people scaling to a much larger research/eng org this year; infrastructure engineers own the training and inference backbone end to end.

• Company: Mission-driven toward general causal intelligence; building a large physics foundation model with weather as the training ground.
• Team: Founders Dar and Kelsie come from self-driving research (Cruise). ~8–10 today, plans to grow to ~35 by EOY, weighted to researchers and infrastructure engineers.
• Role need: ML engineers/researchers with large-scale foundation-model experience; backgrounds in physics, robotics, biology, or AI at the frontier.
• Candidate bar: Must have hands-on experience training large-scale foundation models and be mission-driven; multimodal data and large GPU infrastructure experience is essential.
• Work environment: In-office 5 days/week in SF; explicit emphasis on intensity and long hours (70–80h referenced in screening notes); fast-paced, research-focused.
• Comp & logistics: Relocation support provided; office sized for expansion to ~35.
• Process: 30-min cultural screen 1-hour technical full-day onsite.
• Ideal profile: Intentional and mission-oriented with a clear decision-making history; generalist mindset able to work across researchers and engineers.
• Pain points: Scaling the team fast without lowering the technical or mission-alignment bar.

Own the distributed training and inference backbone for a foundation model trained from scratch — clusters, pipelines, and low-level GPU performance across model scales.

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

• 2–10 years building large-scale ML infrastructure for core foundation models. [Required]

• Experience building ML infrastructure for core foundation models (not fine-tuning). [Must have]
• Worked at a science or physical AI company (e.g. self-driving, robotics, biology). [Required] (recently updated by client)
• Experience as a generalist, working across the ML lifecycle. [Strongly preferred]

• Deep expertise in optimizing large-scale training and inference workloads. [Must have]
• Proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed). [Required]
• Low-level GPU performance optimization and debugging (CUDA + JAX). [Strongly preferred] (recently updated by client)

• Demonstrates high intentionality in career choices and mission-driven focus. [Required]

• Must work in-person 5 days/week in San Francisco (relocation provided). [Must have]

• Experience is only in fine-tuning or deploying models for applications.
• Candidates looking for a slow-paced or 9-to-5 work culture.
• Short tenures (<1.5 years) at multiple recent companies without clear reasons.

• Salary: $200K–$400K
• Equity: Highly competitive early-stage equity
• On-site policy: In-person 5 days/week in the South Park, SF office (relocation provided)
• Visa sponsorship: Open to visa transfers (OPT, H-1B transfers); can sponsor visas
• Employment type: Full-time
• Location: San Francisco, CA

1. Why are you interested in Causal Labs? (mission orientation)
2. Are you actively interviewing with any other companies? Which ones, and on what timeline?
3. How serious are you about joining a startup — judged against who you're actually talking to, not just stated comfort with 5 days onsite / 70–80h / startup intensity?
4. Experience fit: walk through the core-model infra work directly (put specifics in notes).
5. Can you be on-site in SF? If not, are you willing to relocate?
6. What is your salary expectation (base)?
7. How actively are you exploring new opportunities?

Nine Yes/No calibration items on the submission form (all binary — no "?" option is enabled for this role). Overall rating maps to the candidate's score against this JD: Excellent 85+, Good 75–84, Ok 60–74, Poor <60. Only one pending candidate may be marked Excellent at a time.

1. Experience building ML infrastructure for core foundation models (not fine-tuning). [Must have]
2. Deep expertise in optimizing large-scale training and inference workloads. [Must have]
3. Must work in-person 5 days/week in San Francisco (relocation provided). [Must have]
4. 2–10 years building large-scale ML infrastructure for core foundation models. [Required]
5. Worked at a science or physical AI company (e.g. self-driving, robotics, biology). [Required]
6. Proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed). [Required]
7. Demonstrates high intentionality in career choices and mission-driven focus. [Required]
8. Experience as a generalist, working across the ML lifecycle. [Strongly preferred]
9. Experience with low-level GPU performance optimization and debugging (CUDA + JAX). [Strongly preferred]

Submission-form questions requiring written answers. One-liner cap: 140 characters. HM explicitly prefers short, concise answers.

1. Why do you think they'd be a great fit? (recruiter voice — David's perspective)
2. Why are you interested in Causal Labs? (mission oriented) (candidate voice)
3. Are they actively interviewing with any other companies? Which ones? Timeline? (candidate voice)
4. Serious about joining a startup — judged on who they're actually talking to, not just stated comfort with 5 days onsite / 70–80h / startup culture. (candidate voice)
5. Experience fit — explicitly ask the core-model infra questions and put answers in notes. (candidate voice)
6. Base salary expectations. (optional) (candidate voice)

No discrete Ideal Companies list was present on the role page. Background signal below is derived from the intake call, ideal-candidate profiles, and rejected-candidate feedback.

Physical / science AI Self-driving (e.g. Cruise, Waymo-adjacent), robotics, drug discovery / biology, physics-driven modeling.

Large-scale ML systems / accelerators Foundation-model infra teams, ML systems / MLSys groups, AI accelerator companies (e.g. Cerebras, Etched, Annapurna Labs/AWS), large GPU-serving orgs.

For reference only — do not source these specific profiles.

Tong Wu — LinkedIn Staff Software Engineer @ ByteDance | GPU Serving, Optimization, Recommender Systems | Fremont, US

• Strong work on AI infrastructure
• Tech-lead experience but strongly IC-focused

Kamil Khan — LinkedIn Supercomputing @ Etched | Ex-Cerebras, Ex-Google AI | SF Bay Area

• Strong track record shipping AI products in a startup environment

Yuan (Ryan) Li — LinkedIn MLSys @ Annapurna Labs (AWS) | CMU / ZJU alum | Greater Seattle Area

• Top university
• Recent experience in a very similar domain with ownership across the product

• Explicit distributed-training infra: Only submit candidates who can clearly demonstrate hands-on FSDP/DeepSpeed experience and building large-scale foundation-model pre-training clusters (include GPU scale metrics).
• Core model focus: Candidates must be building core model infra, not just inference and post-training pipelines; check for proven low-level GPU profiling/optimization relevant to foundation models.
• Mission & domain fit: Prioritize physical-AI or science-driven backgrounds, and verify willingness for the 5-day onsite commitment and startup intensity.

Observed rejection patterns (HM review, 41 rejections logged): the most common reasons are "lacks large-scale model training infrastructure experience," "focus not aligned with infrastructure role," "not a technical or skill fit," and "mission/company alignment mismatch." Most rejections happen at HM Review — screen hard on core-model infra and demonstrable FSDP/DeepSpeed + GPU-optimization evidence before submitting.