Research Scientist / Engineer — Foundation Model (Agent)
Luma AI is hiring a Research Scientist / Engineer — Foundation Model (Agent) for a remote role open to applicants in United States. Level rates it ; you can apply on Luma AI.
Level's read on this job
- On Level's scale, this research scientist role is Builds AI ●●●●: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include PyTorch, foundation models and multimodal agents.
- Level classifies it as a senior role.
- Level lists 30 other open roles at Luma AI.
AI in this role
Architect, train, and evaluate large-scale multimodal agentic models capable of reasoning, planning, and tool use.
You'll build and train large-scale multimodal agentic models — systems that reason, plan, code, and call tools to do complex, multi-step work over pixels. This is core research shaping how users interact with what Luma's models can do.
It's a multi-stack research role across modeling, data, systems, and evaluation, on novel problems with no existing playbook, treating science and engineering as equally important. It fits someone grounded in foundation models and agentic systems who's trained models at real scale. If you want to work in only one layer of the stack, this deliberately spans several.
What You'll Own
Architect large-scale multimodal agentic models that use reasoning, planning, coding, and tool calling for complex, multi-step work.
Design, build, and run robust data pipelines to construct, enrich, and filter massive pixel datasets, and formulate new tasks.
Train large-scale multimodal models on massive datasets and GPU clusters.
Define and build novel evaluation frameworks to measure multimodal agents.
First 90 Days
One way the first 90 could unfold.
Days 1–30 — Immerse & Diagnose: Learn the current models, agentic approaches, and where evaluation and data are weakest.
Days 30–60 — Ship & Validate: Improve an agentic capability (reasoning, tool use, or coding) and prove it with a new eval.
Days 60–90 — Scale & Systemize: Scale the approach across datasets and clusters and harden the evaluation framework.
What You Bring
Strong foundation in machine learning, foundation models, and agentic systems.
Deep understanding of agentic systems and LLM/VLM reasoning, coding models, and tool calling.
Hands-on PyTorch and large-scale training (distributed, mixed precision, large datasets).
Nice to Have
Experience with state-of-the-art foundation models in reasoning, coding, or tool calling, or state-of-the-art multimodal agents.
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.
How we rate this
Research Scientist / Engineer — Foundation Model (Agent) at Luma AI rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in research scientist jobs at this level
Pay
- Typical research scientist job
- $191k
This posting doesn't list pay. Use the typical research scientist figure when pay comes up. See research scientist jobs
Questions you could be asked
- Tell me about a project where foundation models was part of your work. What did you do?
- Tell me about a project where multimodal agents was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Walk me through how you've used PyTorch in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Foundation Models, Multimodal Agents, Machine learning, Data Pipelines, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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