Luma
Research Scientist / Engineer – Training Infrastructure

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Job Title
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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 parallelism in foundation-model training.
- Deep understanding of GPU clusters, networking, and storage systems.
- Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization.


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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.
Compensation Range
$195K - $395K
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
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