Luma AI
Research Scientist / Engineer – Training Infrastructure
Redwood City, CA
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odds of building a lasting career here
Thin sponsorship signal and lottery-bound (~15% per draw). A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
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the lottery is wage-weighted — each level is one more entry
The bottom of this range ($30,000) is below the Level I prevailing wage of $100,422. An H-1B cannot be filed below the prevailing wage, so an offer at the floor of this band could not be sponsored as posted.
DOL prevailing wage, 2026-27 wage year · Computer and Information Research Scientists (15-1221) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
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About the role
- This is hard PyTorch, CUDA, and distributed-systems work - advanced parallelism, training stability, and utilization across massive clusters.
- It fits an engineer who's solved real problems training foundation models at scale.
- If you haven't worked at the level of FSDP and multi-node training, this is the wrong depth.
Requirements
- Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization.
- Extensive distributed PyTorch training and parallelisms in foundation-model training.
- Deep understanding of GPU clusters, networking, and storage systems.
Nice to have
- Strong Linux systems administration and scripting.
- Experience managing training runs across 100+ GPUs.
- Experience with containerization, orchestration, and cloud infrastructure.
- Luma is an equal opportunity employer.
Compensation
- $30k-$60k
Benefits
- We believe multimodality is critical for intelligence - the next step beyond language models comes from vision.
Company info
- Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
This listing is sourced directly from Luma AI's careers page and normalized into a canonical job model.