TL;DR: Own the machine-learning systems at the core of a live, AI-native fashion product (self-hosted and frontier models, computer vision, and our auto-linking pipeline), and ship plenty of product and backend work alongside them. This is a founding-level role with a path to fully owning AI and a team. We care more about how fast you can learn than about what you already know.
Lekondo is on a mission to create a platform that serves as the ultimate home for the diverse and vibrant cultures that emerge from fashion.
We believe fashion is a universal form of identity, yet its potential as a digital community remains untapped. Our vision is to empower people to connect, express themselves, and celebrate their unique styles through an innovative and inclusive platform.
Based in New York, Lekondo strives to redefine the intersection of fashion and technology with a focus on creativity and community.
Lekondo is backed by world-class investors. The product is live, users upload their outfits every day, and AI is not a feature bolted onto the side. It is the product. Every outfit someone posts is detected, segmented, classified, matched, and turned into a clean catalog of their wardrobe by a pipeline of self-hosted and frontier models. This is a real, production ML system running our own models at scale, not a thin wrapper around someone else's API. Now we need someone to own it, push it, and grow it as the company scales.
You'd join as one of our first handful of engineers, working shoulder-to-shoulder with the founder. This is a rare chance to take ownership of a production AI system that is already live and central to the product, and to grow into fully owning AI and the team beneath it.
This role is roughly half AI/ML and half product engineering, and that split is deliberate.
For the AI half, you'll own the models and pipelines that power the core Lekondo experience: self-hosted LLMs, computer-vision segmentation, embeddings and retrieval, and the auto-linking system that turns a photo of an outfit into structured, deduplicated wardrobe items. You'll have real latitude here, including training or fine-tuning custom models where it earns its keep.
For the other half, you'll ship like the rest of the team. We're small and there's a lot to build, so you'll write backend code well outside of ML: optimizing our Go services, attacking performance, and shipping features end to end. You'll likely lean backend, but if you want to reach into the mobile app and go full-stack, that is welcome too.
As we scale, the AI half grows: you'll fully own the AI roadmap and build and lead the team underneath it. For now, you're a hands-on builder who happens to own the most strategically important surface in the company.
Model strategy. Lekondo runs a mix of frontier APIs and open-weight models (think Gemma and Qwen) on our own GPUs, across both AWS and GCP. We process thousands of outfit uploads a day, and each one fans out into a burst of segmentation, embedding, classification, and generation calls, well over 100,000 model inferences daily that all have to stay fast and cheap as we grow. You'll own the routing that sends each job to the right model, the fallback behavior that keeps the product up when a model (or a whole cloud) has a bad day, and the cost and quality tradeoffs across the entire mix. In short, you'll decide when to self-host, when to call a frontier API, and when to train something custom.
Auto-linking (our flagship ML system). The hardest, most-iterated system we have: a hybrid pipeline of multimodal embeddings, vector search, and LLM re-ranking that matches each garment in a new photo against a user's existing closet. You'll own its precision and recall, its decision thresholds, and its evolution.
Computer vision. Text-prompted segmentation (SAM3), background removal, image generation, and face anonymization, all running on our own GPU clusters across AWS and GCP. You'll own model selection, quality, latency, and cost.
Model quality and evals. The classifiers that read aesthetics, colors, taxonomy, and composition from an outfit. You'll own the prompts, the few-shot calibration, the evaluation harnesses, and the relentless quality tuning that separates "demo" from "production."
The other 50%: product and backend. We're small and there's a lot to ship, so you'll write plenty of code that has nothing to do with ML. That means real backend engineering in Go: the API on ECS, the fleet of Lambdas and Step Functions behind our async pipelines, caching and read-path optimization, and the data modeling that keeps everything fast at scale (DynamoDB & OpenSearch experience is a real plus). You'll ship user-facing features and chase performance wherever it matters. And if you want to reach into the React Native app and go full-stack, that is fair game too.