Machine Learning Engineer (Recommendations)

TL;DR

Machine Learning Engineer (Recommendations): Build high-load, real-time recommendation service and end-to-end ML pipelines driving measurable business impact with an accent on metrics design, model deployment, and production lifecycle. Focus on designing large-scale recommendation and ranking systems, validating impact via A/B tests, and aligning technical roadmaps with business needs.

Location: Fully remote - choose where you live

Salary: $80k–$120k USD, depending on knowledge, skills, experience, and interview results

Company

AI-first ecommerce search and discovery platform that helps global brands drive revenue and conversion gains for hundreds of millions of users.

What you will do

• Build high-load, real-time recommendation services using cutting-edge ML techniques.
• Develop end-to-end ML pipelines that deliver measurable business impact.
• Design metrics to evaluate recommendation relevance and performance.
• Lead full development lifecycle from design to production deployment.
• Participate in strategic planning for product evolution and prioritization.
• Collaborate with stakeholders to align technical roadmaps with business needs.

Requirements

• Deep understanding of ML fundamentals and experience building large-scale recommendation, retrieval, or ranking systems.
• Expertise in Python and SQL, hands-on with big data systems (Spark, Presto/Athena, Hive).
• Production-level ML experience, including model deployment and A/B testing.
• Analytical mindset to translate intuition into data-driven hypotheses and solutions.
• Excellent communication skills in English to explain complex concepts to non-technical stakeholders.

Culture & Benefits

• Unlimited vacation time - encouraged to take at least 3 weeks per year.
• Fully remote team with work from home stipend and Apple laptops provided.
• Training and development budget refreshed annually.
• Maternity & paternity leave for qualified employees.
• Stock options in addition to base salary.
• Regular team offsites for collaboration.