Founding Engineer

The Opportunity

We’re building the future of fashion shopping with AI avatars: personalized try-on, social discovery, and interfaces that make shopping feel alive.

We’re a small, NYC-based team reimagining fashion shopping from the ground up. Our vision is playful, deeply personal, and centered on self-expression. To bring it to life, we combine advanced AI research, meticulous attention to detail, and an intuitive grasp of contemporary culture.

Previously we’ve built products used by millions at Apple, DeepMind, Meta, Shopify, and startups. Now we’re backed by the investors behind OpenAI, Cursor, and SKIMS.

The Role

We’re looking for a founding engineer who can move fast across the stack and go deep when the problem demands it. Early on, the work will be varied: one week you might ship a scrappy 0-to-1 prototype, the next you might optimize a feed ranker, debug a slow endpoint, or build infrastructure the rest of the team depends on. Over time, as the company grows, you’ll own the areas where you’re strongest.

This is an in-person role from our office in NYC.

The best person for this role is T-shaped: broad enough to move across AI, backend, data, and product surfaces when needed, with real depth in at least one of these areas:

• AI product systems - diffusion pipelines, batch LLM workflows, vector search, evals, structured outputs, and reliability work under real product constraints.
• Recsys and personalization - retrieval and ranking systems, embedding-based search, learned rankers, sequence modeling of user behavior, and online experimentation.
• Core product infrastructure - databases and query optimization, data warehousing, infra-as-code, deployment, observability. You know where latency, reliability, and cost problems usually hide.

As a Founding Engineer you may

• Build end-to-end prototypes fast
• Go deep when necessary: optimize the feed ranker, profile a slow endpoint, debug a gnarly race condition, chase down a memory leak
• Own personalization end-to-end: retrieval, ranking, cold-start, and the tradeoffs that make recommendations feel fresh and relevant
• Ship infrastructure the team builds on top of: data pipelines, internal tools, eval systems
• Build LLM-powered systems that actually work in production
• Work directly with the founders to turn early product ideas into working software
• Contribute to product strategy and figure out what's worth building

You're a great fit if you

• Can move between scrappy prototyping and deep technical work
• Ramp fast on unfamiliar domains
• Have taste for consumer products and opinions about what makes them good
• Have worked on consumer products, social products, marketplaces, or other user-facing systems at meaningful scale
• Are self-driven, high-agency, and good at getting unstuck
• Have genuine interest in fashion, avatars, or creative expression

Bonus if you

• Have shipped iOS or other native mobile experiences
• Have worked on the infrastructure side of AI/ML - training pipelines, eval systems, data flywheels