Research Scientist / Engineer – Training Infrastructure
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 78d ago
The date the source published, not the day we noticed it (2026-07-24). Last seen at its source 2h ago.
We have tracked this listing since 13 Aug 2026 (58 days). The employer's own board has carried it every time we have read it since 19 Aug, when our records begin, most recently 2 hours ago.
Is it remote?
Remote, US
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $150k–224.8k/yr
Middle 50% of 4027 listings that do state pay — Engineering · all levels · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 58d ago · last seen 2h ago
- posted 2026-10-08
The listing
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.