Machine Learning Engineer (Revenue+)

Location: New York, United States; office attendance expected 4+ days per week

Salary: $209,000–$313,000 annual base salary for Zone A, including NYC; other US pay zones range from $178,000–$266,000 and $199,000–$297,000 annually. Eligible for RSUs.

Company

hirify.global builds hirify.globalchat and other digital products used by hundreds of millions of people, with a focus on visual communication, privacy, and human-centered computing.

What you will do

• Identify opportunities to improve subscription growth, monetization, retention, and subscriber value through machine learning.
• Design, build, and deploy ML systems for personalization, ranking, propensity modeling, offer decisioning, and lifecycle optimization.
• Lead the full ML lifecycle from ambiguous business problems and data exploration through experimentation, production deployment, measurement, and iteration.
• Establish technical direction and best practices for the Revenue+ ML capability.
• Partner with Product, Data Science, Backend, and Mobile Engineering to shape product strategy and prioritize ML investments.
• Build reliable, observable, scalable production ML systems and use AI tools while maintaining code quality, security, and architectural integrity.

Requirements

• Bachelor’s degree in a relevant technical field or equivalent practical experience; advanced degree options may qualify with fewer years of experience.
• At least 5 years of post-bachelor’s machine learning experience, or equivalent experience with a master’s or PhD.
• Experience developing and productionizing ML systems for ranking, recommendation, personalization, propensity modeling, decisioning, monetization, retention, or related product applications.
• Experience taking ML systems from ambiguous problem statements through experimentation and into production.
• Strong machine learning, software engineering, product judgment, collaboration, mentorship, and technical ownership skills.
• Ability to work from the New York office at least 4 days per week under the default-together policy.

Nice to have

• Experience with subscriptions, monetization, pricing, offers, retention, or lifecycle optimization.
• Experience with causal inference, uplift modeling, experimentation, customer lifetime value, or incremental impact measurement.
• Experience collaborating extensively with Backend and Mobile software engineers.
• Experience operating production ML systems at significant scale or serving as an early or founding ML engineer.

Culture & Benefits

• In-person collaboration through the default-together workplace approach.
• Paid parental leave and comprehensive medical coverage.
• Emotional and mental health support programs.
• Compensation package including equity through RSUs.
• Commitment to privacy, diversity, inclusion, and equal employment opportunity.