Research Scientist / Engineer – Training Infrastructure

Luma

Remote, EU

Remote

Who this role is best for

Best suited to candidates with deep experience in distributed PyTorch training and foundation-model parallelism working in AI research infrastructure.

Best fit for

Candidates with hands-on experience in FSDP and multi-node training for large-scale models
— “If you haven't worked at the level of FSDP and multi-node training, this is the wrong depth.”
Engineers with a background in optimizing GPU clusters and distributed systems
— “Deep understanding of GPU clusters, networking, and storage systems.”

Things to consider

The role requires a strong grasp of advanced parallelism techniques, not just basic knowledge
— “advanced parallelism, training stability, and utilization across massive clusters”
The candidate must be capable of working on complex, large-scale infrastructure systems
— “train Luma's large-scale multimodal models across thousands of GPUs”

How to stand out

Showcase experience in building training tools for large-scale systems
— “Build monitoring, visualization, and debugging tools for large-scale training runs.”
Highlight your ability to optimize training stability and resource usage in distributed environments
— “Optimize training stability, convergence, and resource utilization across massive clusters.”

Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

designing and implementing efficient distributed training systems
optimizing training stability and convergence

Typical background

experience with distributed PyTorch trainingunderstanding of GPU clusters

Skills & requirements

Required

Distributed Pytorch TrainingParallelismGPU ClustersNetworkingStorage SystemsCommunication Libraries

Preferred

Linux Systems AdministrationContainerizationOrchestration

Stack & domain

PyTorchCUDADistributed SystemsFSDPTensor ParallelPipeline ParallelExpert ParallelMonitoringVisualizationDebuggingLinux Systems AdministrationScriptingContainerizationOrchestrationCloud InfrastructureProblem-solvingTeamworkCommunicationAIMachine LearningEngineering

About the role

Original posting from Luma via Ashby

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.

Source: Luma careers (Ashby)