Serve as the technical lead for an ML team focusing on personalization, relevance, and ranking. Own the end-to-end architecture, from model training and evaluation to deployment and scaling in production.
About the Role
Fetch is building the future of personalized consumer experiences. We’re looking for a Staff Machine Learning Engineer to serve as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking.
In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale. You’ll partner closely with product and cross-functional stakeholders while remaining deeply hands-on in design and development.
Role Responsibilities
Serve as the technical lead for a single ML-focused team, setting direction and raising the bar on engineering quality and system design.
Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases.
Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving.
Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions.
Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability.
Partner closely with product, data, and engineering stakeholders to deliver measurable business impact.
Mentor engineers through hands-on technical guidance, feedback, and example.
Use AI tools to accelerate development and improve system design, including:
Prototyping and validating ideas with LLM tools.
Leveraging AI for code iteration and experimentation.
Using AI assistants for architecture diagramming and design validation.
Exploring LLM-powered features where appropriate.
Minimum Requirements
5+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale.
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.
Demonstrated ownership of complex technical initiatives within a team.
Strong systems design skills with the ability to clearly articulate tradeoffs and implementation decisions.
Experience mentoring engineers and influencing technical standards within a team.
Ability to operate effectively in ambiguous environments and drive projects to completion.
Preferred Requirements
Familiarity with LLMs and their application in personalization, feature generation, or search.
Experience with real-time or streaming ML systems.
Exposure to experimentation frameworks (A/B testing) and model performance measurement.
Experience bridging model development with real-time serving systems.
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