Technical Lead Manager, AI Platform

Paraform•San Francisco, CA

1d•Hybrid

About The Position

The AI Platform team is responsible for the matching, ranking, and agentic systems that are crucial to the company's product. This role involves leading a team of applied AI and ML engineers while remaining hands-on with core ML work, including data and model design, and tackling complex ranking and matching challenges. The position requires close collaboration with product and data science teams, with accountability for key outcomes such as match quality, automation rate, and overall hiring velocity. The role emphasizes a balance between technical leadership and hands-on development, with expectations for engineers to ship production code regularly. The company is experiencing significant growth, scaling the engineering team substantially.

Requirements

Staff-level IC depth in ML or applied AI.
1+ years managing or formally tech-leading engineers (Tech Lead Manager) OR 4+ years including setting technical direction across multiple teams (Technical Director).
Experience shipping production ML or LLM systems used by real people at scale (retrieval, ranking, recommendation, agentic workflows, or similar).
Proficiency in eval-driven development, measuring model quality against business outcomes.
Data fluency, including SQL, experiment analysis, and metric evaluation.
Experience using AI tools to enhance team leverage.
Strong written communication skills for operating norms, decision docs, and postmortems.
Willingness and ability to write production code weekly.

Responsibilities

Manage, coach, and develop a team of applied AI and ML engineers.
Lead the development of core ML systems including matching, ranking, and retrieval models.
Take ownership of challenging ML problems, such as improving plateaued ranking models or addressing cold-start issues.
Develop and refine matching and ranking models that directly impact marketplace revenue.
Build and maintain LLM-powered agentic systems for workflow automation.
Implement and manage trust and safety systems, including fraud detection and submission quality enforcement.
Own and develop evaluation frameworks to measure model quality, reliability, and business impact.
Optimize the balance between quality, cost, and latency in AI systems.
Develop and execute a quarterly roadmap based on business goals.