Software Engineer - Product

About Luma

When there’s a new wave of technology, you want to be at the core—that’s where you have the most impact, work on the most challenging problems, and grow the most.

Not at a startup wrapping someone else’s API. At the company building the models themselves. The models are where the real innovation is happening. They are replacing entire categories of software—and enabling entirely new ones.

At Luma, we are:

• A leading multimodal AI research lab, having built one of the world’s strongest video generation models (Ray-3.14)
• Pushing beyond video toward the next generation of multimodal general intelligence models
• Operating at a scale few companies can match, with the compute and resources to support frontier research ($900M Series C)
• Focused on the creative domain, where multimodal systems can have immediate real-world impact
• Shipping tightly integrated products that turn research breakthroughs into tools creators actually use

We give engineers real ownership early—and the chance to grow faster than almost anywhere else.

Where You Come In

We’re looking for engineers early in their arc who are already shipping at a high level. AI has lowered the cost of building. That creates an opportunity for engineers with high agency and fast learning speed to take on meaningful ownership much earlier than before.

You may already have senior-level impact. What you likely haven’t accumulated yet are years of production scars—large-scale failures, long-lived architectural consequences, and irreversible bets.

That’s what you’ll gain here.

You’ll work on real products, ship to real users, and operate in close proximity to experienced technical leaders. You’ll be expected to move fast, take ownership, and learn by doing.

This is not a role where you wait for tickets. It’s a role where you build.

What You’ll Do

• Use AI coding agents to ship features and products at a pace that would have been impossible a few years ago
• Work directly with product, design, and engineering teams to turn ideas into working software
• Learn rapidly across multiple domains—frontend, backend, systems, product, design
• Own meaningful product outcomes end-to-end
• Ship fast, get feedback, iterate, and improve
• Build your judgment about architecture, security, UX, and trade-offs by doing

Minimum Qualifications

• Bachelor’s degree in Computer Science or equivalent practical experience
• Experience building and shipping software (internships and substantial personal projects qualify)
• Strong proficiency in at least one general-purpose programming language (e.g., Python, TypeScript, Go, Java)
• 2+ years of professional software engineering experience, or can provide evidence of exceptional ability demonstrated through at least one standout accomplishment, such as:
• Owning and delivering a non-trivial software system or feature
• A substantial personal project with real users or adoption
• Success in a competitive programming contest, hackathon, or technical competition
• A significant open-source contribution or widely used library

What We Look For

• A demonstrated pattern of building software and shipping real work
• Evidence of learning new tools, patterns, or domains by building real things
• Experience using AI coding tools to build or ship non-trivial functionality
• Strong programming fundamentals (data structures, algorithms, systems thinking)
• Examples of ownership—identifying problems, taking initiative, and seeing work through

This role is defined by learning speed and output, not years of experience.

Why This Role Exists

Most early-career engineering roles optimize for gradual progression. This one compresses the curve.

AI has lowered the cost of building. That creates space for high-agency engineers to take on meaningful ownership much earlier than before.

The work you do here will shape:

• How quickly research becomes product
• How fast we can iterate
• How the next generation of engineers grows inside an AI-native company

If you want to accelerate your growth by shipping real systems at real scale, we should talk.