Research Scientist / Engineer – Training Infrastructure

Luma AI is hiring a Research Scientist / Engineer – Training Infrastructure for a remote role open to applicants in United States. Level rates it ; you can apply on Luma AI.

Level's read on this job

Builds AI ●●●● is how Level rates this research scientist role: the job is building AI systems.
This is listed as a remote role.
The posting mentions PyTorch, CUDA and Nccl among its tools and skills.
Level classes it as a senior role.
Luma AI has 30 other open roles on Level.

AI in this role

Design, implement, and optimize distributed training infrastructure and parallelization systems for large-scale multimodal models.

pytorchcudancclmpilinux

distributed-systemsgpu-optimizationparallelisminfrastructure

You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.

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.

What You'll Own

Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs.
Research and implement advanced parallelization (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel).
Build monitoring, visualization, and debugging tools for large-scale training runs.
Optimize training stability, convergence, and resource utilization across massive clusters.

First 90 Days

One way the first 90 could unfold.

Days 1–30 — Immerse & Diagnose: Learn the current training stack and where stability and utilization hurt at scale.
Days 30–60 — Ship & Validate: Land a parallelization or stability improvement that measurably helps a real training run.
Days 60–90 — Scale & Systemize: Build the monitoring and tooling that keeps large runs reliable and efficient.

What You Bring

Extensive distributed PyTorch training and parallelisms in foundation-model training.
Deep understanding of GPU clusters, networking, and storage systems.
Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization.

Nice to Have

Strong Linux systems administration and scripting.
Experience managing training runs across 100+ GPUs.
Experience with containerization, orchestration, and cloud infrastructure.

About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.

How we rate this

Research Scientist / Engineer – Training Infrastructure at Luma AI rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Research Scientist / Engineer – Training Infrastructure is an open job at Luma AI.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Redwood City, CA and is marked remote.
The job was posted on July 24, 2026.
Seniority: senior.
Applications are handled by Luma AI. Apply on Luma AI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Distributed SystemsGPU OptimizationParallelismInfrastructurePyTorchCUDANcclMpi

Also common in research scientist jobs at this level

Machine learning Python LLM Deep learning Reinforcement learning Generative AI

Pay

Typical research scientist job: $191k

This posting doesn't list pay. Use the typical research scientist figure when pay comes up. See research scientist jobs

Questions you could be asked

Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where gpu optimization was part of your work. What did you do?
Tell me about a project where parallelism was part of your work. What did you do?
Tell me about a project where infrastructure was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Distributed Systems, GPU Optimization, Parallelism, Infrastructure, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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