Research Scientist / Engineer – Training Infrastructure
LocationRemote, EU
Work moderemote · United Kingdom
Typefull-time
SenioritySenior
DepartmentResearch & AI
Company size201–500 people
First seenOct 10, 2026 · 1d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Must have3
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 have3
Strong Linux systems administration and scripting
Experience managing training runs across 100+ GPUs
Experience with containerization, orchestration, and cloud infrastructure
Skills
PyTorch
CUDA
FSDP
Tensor Parallel
Pipeline Parallel
Expert Parallel
NCCL
MPI
Linux
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.
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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 the company
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.