Director of Engineering, Machine Learning Recommendations

Location: Hybrid in Brooklyn, New York, US; candidates are prioritized within commuting distance of hirify.global’s Brooklyn office and are expected to come in once or twice per week. Remote candidates outside commuting distance may be considered case by case, with occasional travel to the office.

Salary: Geo-1: $296,000–$400,000 annual; Geo-2: $266,000–$360,000 annual. Additional compensation includes equity and an annual performance bonus.

Company

hirify.global is a global marketplace for unique and creative goods, building tools and technologies that connect entrepreneurs with buyers.

What you will do

• Define and drive the multi-year product and technical roadmap for Recommendations ML, focusing on personalization, foresight, diversity, and content freshness.
• Lead and grow ML engineering, applied science, and platform engineering teams across candidate retrieval, ranking, and recommendation systems.
• Own the end-to-end technical strategy for personalized discovery across the App Homefeed, Web Home, Shop Home, Listing pages, Cart, and Checkout.
• Partner with Product, UX, Search, Ads, Buyer Understanding, and Content Understanding teams on buyer experience and shared infrastructure.
• Drive the evolution from two-stage retrieval and ranking toward generative and LLM-integrated recommendation architectures.
• Set standards for model development, experimentation, evaluation, productionization, and talent development.

Requirements

• 10+ years of progressive experience in ML engineering.
• Demonstrated success shipping large-scale recommendation, retrieval, or ranking systems into production.
• Experience leading and developing multidisciplinary teams of ML engineers, applied scientists, and platform engineers, including managing managers and senior managers.
• Experience driving complex, cross-functional technical programs to completion, including shared infrastructure decisions with Search, Ads, or adjacent ML platform teams.
• Ability to balance near-term product impact with mid- to long-term research investment and communicate trade-offs to engineering and product leadership.
• Familiarity with large-scale ML infrastructure, including distributed training with TensorFlow and PyTorch, feature stores, model serving, and online experimentation frameworks.

Culture & Benefits

• Work in a diverse, equitable, and inclusive workplace focused on meaningful marketplace impact.
• Benefits support employees and their families.
• Equity package and annual performance bonus are available in addition to salary.
• Development opportunities extend beyond promotions, with an emphasis on career growth and collaborative relationships.
• Visa sponsorship is supported, although availability may be limited to certain roles and skills.

Hiring process

• Submit a resume; a cover letter is optional.
• Accommodation is available during any part of the interview or hiring process upon request.