Recruit senior technical roles in Data Infrastructure and Machine Learning Engineering, collaborating with hiring managers and sourcing candidates from diverse platforms.
About Luma AI
About the Role
What You'll Do
A Day in the Life
What We're Looking For
Bonus Points
Luma’s mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable, and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change. Our flagship product is Dream Machine, and we recently released Ray3, the first reasoning video model. We're ~110 people, growing fast, and Applied Research is a big part of that growth.
About the Role
We're building out the Applied Research recruiting team at Luma. AR sits between the "Lab" and the "Product" - the team that turns raw research into tools that millions of people actually use.
Right now, the hiring focus is Data Infrastructure and Machine Learning Engineering. As we scale, that expands into Research Engineers, Applied Scientists, Graphics Engineers, and Technical Artists.
We're hiring multiple senior recruiters who can hit the ground running and help build the function from the ground up.
- Recruit for Data Infra and ML Engineering roles (for now), with scope expanding as the team grows
- Partner with hiring managers to define what "good" looks like in a field that's evolving fast
- Source beyond LinkedIn - GitHub, Twitter, academic labs, niche communities. The best candidates often aren't job hunting.
- Evaluate candidates by what they've built, not just where they've worked. Portfolios, side projects, and GitHub matter here.
- Move fast. AR is the velocity engine of Luma. You match that pace.
- Hybrid in Palo Alto (~3 days/week, flexible)
A Day in the Life
- Morning sync with the AR recruiting team on pipeline and priorities
- Deep sourcing block - you're in GitHub, Twitter, or a niche ML community looking for candidates who aren't applying anywhere
- Intake call with a hiring manager for a new ML Eng role. You're helping them figure out what "great" looks like, not just taking a req.
- Candidate screens - you're evaluating what they've built, not just where they've been
- Afternoon spent on outreach. You're testing new messaging, maybe using AI to personalize at scale.
- Quick debrief on an onsite. You're pushing for signal, not just "thumbs up."
- End of day: update your pipeline, flag blockers, prep for tomorrow.
Some days you're heads down sourcing. Some days you're in back-to-back screens. Some days you're working on a new sourcing strategy or tool. It moves fast.
- 4+ years recruiting for technical roles - specifically ML, Data Infra, Research Engineering, or similar. Not generic SWE.
- You know the difference between a Researcher and an Applied Engineer. You can speak to what makes a good ML Engineer vs. a good Data Infra Engineer.
- You're a builder. You don't wait for permission to try a new sourcing strategy or tool. High agency.
- You use AI in your workflow - for outreach, research, automation, whatever. It's a co-pilot, not a threat.
- You've worked at a startup or high-growth company. You're comfortable with ambiguity and shifting priorities.
- You've recruited for gaming, VFX, or creative tools companies
- You've hired Research Engineers, Applied Scientists, Technical Artists, or Graphics Engineers
- You have a personal interest in generative AI, creative technology, or the tools we're building
- You come from a top AI lab or frontier company (OpenAI, Anthropic, DeepMind, NVIDIA, etc.)
The base pay range for this role is $150,000 – $260,000 per year.
About LumaLuma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
Top Skills
AI
Data Infrastructure
Machine Learning
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