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Lumaai

Research Scientist / Engineer – Training Infrastructure

  • Seen live on Lumaai's site today
  • Mid level
  • Remote
Pay
Not stated
Location
Remote, EU
Open for
12+ days
First seen
Oct 23, 2026
Apply on Lumaai's site (opens in a new tab)

Posting record

No flags

Open 12+ days

The posting's record, oldest first
  1. Lumaai's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Lumaai, so its true age is longer.

Last checked Nov 4, 2026.

How well do you match?

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What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

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.

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