Machine Learning Engineer

Turtles; United States; Hybrid; USD 120,000 - 160,000; Full time

Job summary

Turtles is looking for a Machine Learning Engineer to design and own data-driven systems, including recommendation and promotion engines, impacting company-level metrics directly.

The ideal candidate will have over 4 years of experience in applied ML with strong fundamentals in modeling and engineering. You will work across teams and drive ML innovations for 29 million shoppers in our marketplaces.

This role offers the chance to make significant contributions in a dynamic and fast-growing environment.

Qualifications

• 4+ years of applied ML experience in production.
• Strong fundamentals in modeling, evaluation, and engineering.
• Ability to work comfortably across the tech stack.

Responsibilities

• Design, build, and own ML systems from data pipeline to serving.
• Drive recommendation, ranking, and personalization across marketplaces.
• Build models for promotion targeting and ad bidding.

Skills

Machine Learning

SQL

Python

Data Pipeline

Job description

We’re looking for a machine learning engineer to own data-driven systems across the company: recommendation engines, promotion engines, ad-buying and measurement, and whatever the next high-leverage ML surface turns out to be. You’ll work across teams and own real production systems.

About the team

Turtles is a world-class team letting anyone turn their passion into a beloved marketplace, helping 29M shoppers find the things they’ll love. We’re one of the fastest-growing startups in the US and we’re hiring brilliant and exceptionally-driven people to join us. We have a lean, 60 person team where every person owns outcomes end-to-end, learns and grows incredibly quickly, and works with teammates who set high standards. Our culture is focused around rapid experimentation, lean autonomous teams, and minimal bureaucracy. Turtles isn’t for everyone, and that’s intentional. If you’re energized by hard problems and big opportunities, you’ll thrive here.Our team is primarily split between our offices in NYC, Helsinki, and Hong Kong, with portions of our team also working remotely. We prefer in-person hires and indicate our preferred locations for each role, but please apply regardless of your location as we’re always looking for brilliant people to join. We’re excited to meet you.

Why this role matters

ML is one of the few areas where a single engineer can move company-level metrics. Recommendations, promotions, and ad measurement are all multi-million-dollar levers. What you build directly shapes how 29M shoppers discover and how marketplaces grow.

What you’ll do

• Design, build, and own ML systems in production, from data pipeline to serving.
• Drive recommendation, ranking, and personalization across the marketplaces.
• Build models for promotion targeting, ad bidding, and measurement.
• Partner with product, growth, and marketing on the highest-leverage data problems.
• Set the bar for evaluation, observability, and iteration speed in ML.

What we’re looking for

• 4+ years of applied ML in production, ideally end-to-end ownership.
• Strong fundamentals: modeling, evaluation, and engineering for serving.
• Comfortable across the stack: SQL, Python, model serving, basic infra.
• Track record of moving real metrics, not just publishing notebooks.

Nice to have

• Experience with recommendation, ranking, or marketing-mix modeling.
• Experience working closely with growth or marketing teams.

How we hire

A short, focused process designed to respect your time. The specifics vary by role, but here’s the format we typically aim for:

• Intro call with a relevant team member or a founder (30 min).
• Working session: pair with a senior engineer or a founder on a real recommendation, ranking, or ML systems problem (~60 min).
• Depending on the role, a paid half-day trial so we can see how you work with the team, and so you can see what the problems actually look like.
• Offer within 5 business days of the final round.

Turtles is an equal-opportunity employer. We hire people from every background and we’re committed to building a team that reflects the diversity of the shoppers, brands, and marketplaces we serve.