Lead ML Engineer – Personalization & Ranking (Remote)

fetch; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Fetch is seeking a Staff Machine Learning Engineer to act as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking. You will own the technical direction and execution for ML systems, guiding architecture and deployment while staying hands-on in design and development.

You will partner with product, data, and engineering stakeholders to deliver measurable business impact, mentor engineers, and explore LLM tools to accelerate development and improve system

Qualifications

• 12+ years of industry experience in machine learning or software engineering.
• Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains.
• Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure.
• Experience deploying ML models into production and operating them at consumer scale.

Responsibilities

• Serve as the technical lead for a single ML-focused team, setting direction and engineering quality.
• Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases.
• Own end-to-end architecture for services, including model training, evaluation, deployment, and serving.
• Drive clarity in ambiguous problem spaces and translate product needs into scalable technical solutions.
• Lead design reviews and balance latency, reliability, experimentation, and maintainability.

Skills

Machine Learning

Distributed Systems

Leadership

Production ML

Mentoring

Education

Bachelor's degree in CS/Engineering or related

Job description

Fetch is seeking a Staff Machine Learning Engineer to act as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking. You will own the technical direction and execution for ML systems, guiding architecture and deployment while staying hands-on in design and development.

You will partner with product, data, and engineering stakeholders to deliver measurable business impact, mentor engineers, and explore LLM tools to accelerate development and improve system