Job Description

• 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.

• 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.

• 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.