Technical Lead Manager, AI Platform

Paraform•San Francisco, CA

4d•$260,000 - $340,000

About The Position

Paraform is hiring a technical leader to own its AI Platform engineering team. This is a hands-on technical leadership role managing a pod of Applied AI, ML, and Product engineers while remaining deeply involved in core machine-learning work, from data and model design to difficult matching and ranking problems. The role partners closely with Product and Data Science and is accountable for outcomes such as match quality, automation rate, and customer hiring velocity. The manager is expected to spend at least a third of their time building, and more during the early days. They will read pull requests, review evaluations, and ship production code every week. The management load will grow with the team, while the technical standards the leader personally sets remain central to the role. The title may vary by experience (Tech Lead Manager, Technical Director, or Engineering Manager), but ownership remains the same. The role leads the team, builds and develops its engineers, and owns the technical direction and delivery of AI systems powering Paraform's recruiting marketplace. This includes matching, ranking, retrieval, personalization, agentic workflows, trust and safety, evaluation frameworks, and system quality, cost, and latency. The leader turns ambiguous business goals into quarterly plans and ships systems that improve marketplace outcomes. Paraform describes itself as an end-to-end AI recruiting marketplace connecting companies with specialized recruiters and AI agents. Its mission is to make hiring fast, reliable, and scalable for every company.

Requirements

Staff-level individual-contributor depth in machine learning or applied AI, with a record of staying technical while leading.
At least 1 year managing or formally tech-leading engineers for a Tech Lead Manager path, or at least 4 years including setting technical direction across multiple teams for a Technical Director path.
Experience shipping production ML or LLM systems used by people at scale, such as retrieval, ranking, recommendation, or agentic workflows.
Ability to measure model quality against business outcomes, evaluate beyond offline benchmarks, and distinguish genuine improvements from noise.
Data fluency, including writing SQL, sanity-checking experiment readouts, and challenging flawed metrics.
Use of AI tools in personal work and ability to increase team leverage with them.
Clear written communication, including writing operating norms, decision documents, and postmortems.
Willingness to continue writing production code every week.

Responsibilities

Hire, coach, and level a pod of Applied AI, ML, and Product engineers.
Remain hands-on in core ML work, including data and labeling strategy, feature and embedding design, model selection, training, and fine-tuning.
Take on and ship fixes for difficult or ambiguous modeling problems, including stalled ranking improvements, offline evaluations that conflict with production results, and cold-start problems.
Own matching, ranking, retrieval, and personalization systems that connect marketplace supply and demand.
Build LLM-powered workflows and automation for recruiters, hiring managers, and internal operations, with emphasis on reliability as well as capability.
Own trust and safety systems for fraud and spam detection, submission quality enforcement, and guardrails for accurate and fair AI outputs.
Own evaluation frameworks for measuring model quality, reliability, and business impact in production, and build the team's evaluation practices.
Make decisions that balance system quality, cost, and latency, including choosing between LLMs and traditional ML where appropriate.
Translate ambiguous business goals into a quarterly plan and deliver it.
Set the technical direction for AI at Paraform.
Read pull requests, review evaluations, and ship production code regularly.