Description review
Research Scientist / Engineer – Training Infrastructure
Luma AI · Redwood City, CA, Remote, US, Remote, EU, New York, NY, Remote, INT, Seattle, WA, London, UK, Remote, UK · back to the listing
HR standards
61/100
needs work
Title ↔ description
78/100
solid
Reads as
Machine Learning Engineer
96% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.