Machine Learning Engineer, Search, Ads & Recommendations

Location: Hybrid role based within commutable distance of hirify.global’s Brooklyn office, with in-office attendance once or twice per week; remote candidates outside commuting distance may be considered case by case and must travel to the office occasionally.

Salary: $182,000–$246,000 annual in Geo-1 or $164,000–$222,000 annual in Geo-2, depending on location, skills, and experience.

Company

hirify.global operates a global marketplace connecting millions of entrepreneurs and buyers through tools and technologies for unique and creative goods.

What you will do

• Build and productionize machine learning systems for search ads, recommendation ranking, or search retrieval at hirify.global scale.
• Own models end to end, including data and feature pipelines, training, evaluation, production serving, monitoring, and iteration.
• Apply machine learning and information retrieval techniques to improve relevance, personalization, buyer engagement, and seller outcomes.
• Design and run online and offline A/B experiments to evaluate model effectiveness, efficiency, latency, and buyer impact.
• Investigate model and system behavior, resolve nuanced issues, and measure product improvements with product and analytics partners.
• Work cross-functionally with software engineers, applied scientists, data scientists, product managers, designers, and analytics partners.

Requirements

• Track record of applying statistical and machine learning techniques to real-world problems.
• Expertise or experience in one or more of information retrieval, search, recommendations, ads, natural language processing, deep learning, multi-modal learning, or reinforcement learning.
• Hands-on experience training, deploying, monitoring, debugging, and iterating on production machine learning systems in search, recommendation, and/or ads.
• Production experience building large-scale machine learning systems in Python, Java, Scala, or similar languages, using modern machine learning frameworks such as PyTorch or TensorFlow/TFX.
• Ability to write and manipulate data at large scale and produce understandable, testable, maintainable code.
• Ability to create requirements and lead the team from design through delivery.
• Collaborative and empathetic approach with clear, constructive communication across disciplines.
• Concern for the implications of machine learning work on end users.
• M.S. or Ph.D. in Computer Science or a related engineering field with at least two years of practical machine learning experience, or equivalent experience.

Culture & Benefits

• Full-time employment reporting to the Engineering Manager of the assigned team.
• Equity package and annual performance bonus in addition to salary.
• Competitive benefits supporting employees and their families.
• Inclusive workplace committed to diversity, equity, equal opportunity, and candidate accommodations.
• Visa sponsorship is supported, although availability may be limited to certain roles and skills.

Hiring process

• Team placement is based on background, level, and interests and is discussed during the interview process.
• Candidates submit a resume and may optionally include a cover letter.