Staff ML Engineer

About Alta: Alta is building the future of shopping with AI.

We have raised $31M+ from Forerunner, Menlo Ventures, LVMH and consumer technology founders.

The role: At Alta, the models are the product. They help people decide what to wear and what to buy, every day. You'll own the models you build end to end and make them better in ways users can feel as well as the engineering foundation under them.

What you'll work on:

• Teaching models style. Style is personal and subjective, and it changes with the day, the season, and the person.
• Understanding the real world. People's clothes, their photos, their days. None of it arrives clean, and getting it right is a large part of the job.
• Personalization. Getting someone's first session right on almost no history, then improving with every session after.

This is a fit if you:

• Are kind. You make the people around you better.
• Are scrappy. You find the fastest way to learn whether an idea works.
• Take full ownership. You own a model from its first label to its health in production.
• Are intentional. You know what success looks like before you start, and you trust the result when it disagrees with you.

How we work at Alta:

• Trustworthiness. We say what we know and what we don't, and we report results as they are.
• Work hard. We hold a high bar and finish what we start.
• Continuous improvement. We ship, measure, and change course when the data says to.
• Customer obsession. Everyone talks to users, including the ML team.

Responsibilities:

• Own production models end to end: data, training, serving, monitoring, and cost.
• Design experiments, read them honestly, and make the ship/kill call.
• Work closely with product, design, and growth, and talk to users about why a recommendation felt wrong.

Requirements:

• 6+ years of ML engineering or applied ML experience, have developed models end-to-end in production with real users.
• Experience setting the technical direction for an ML area.
• Deep experience with deep learning and the engineering around it: training, serving, and data pipelines.
• Experience in recommendations, vision, or generative models.
• Sound experiment design and statistics.

Experiences we look for:

• You've read raw data by hand and found what the metrics missed.
• You've seen an offline win fail online and learned why.
• You've built an eval set from scratch for something subjective.
• You've chosen not to train a model, and it was the right call.

Nice to have:

• Fashion, e-commerce, or consumer recommendation experience.
• Consumer AI products you've built.

Location: In-person in NYC, five days a week.

Compensation: $200-275k base + Alta equity + healthcare benefits.