Sr. Engineering Manager, AI and Agents

GoodRx•San Francisco, CA

7d•$169,000 - $361,000•Onsite

About The Position

GoodRx is the leading prescription savings platform in the U.S. Trusted by more than 25 million consumers and 750,000 healthcare professionals annually, GoodRx provides access to savings and affordability options for generic and brand-name medications at more than 70,000 pharmacies nationwide, as well as comprehensive healthcare research and information. Since 2011, GoodRx has helped consumers save nearly $75 billion on the cost of their prescriptions. Our goal is to help Americans find convenient and affordable healthcare. We offer solutions for consumers, employers, health plans, and anyone else who shares our desire to provide affordable prescriptions to all Americans.

Requirements

12+ years of software engineering experience, including experience building AI/ML or GenAI systems in production and 3+ years managing or leading engineers and engineering teams.
Demonstrated ability to lead multiple highly technical engineering teams and technical leaders while driving effective execution across related teams and initiatives.
Deep technical fluency with LLM application architecture: RAG, agents and tool use, prompt engineering, fine-tuning trade-offs, evaluation methods, and MLOps / LLMOps, with experience developing and scaling reusable AI platforms, agent frameworks, evaluation systems, and observability practices.
Hands-on experience shipping and operating production AI services, including APIs, orchestration, cloud infrastructure, cost and latency optimization, and monitoring at scale.
Track record of building and leading high-performing engineering teams in a matrixed organization, including blended employee and contractor teams, with demonstrated experience developing engineers and engineering leaders.
Experience designing evaluation and behavioral measurement for AI systems and using those results to gate releases, improve quality, and inform ongoing technical and product decisions.
Excellent communication and influence skills, with demonstrated ability to explain complex technical strategy, drive alignment across teams and organizational boundaries, and influence senior stakeholders through complex technical tradeoffs and decisions, including presenting effectively to executive audiences when appropriate.
Bachelor’s degree in computer science or a related technical field, or equivalent experience; advanced degree a plus.

Nice To Haves

Healthcare, pharma, finance, or other regulated-industry experience.
Familiarity with AI governance frameworks (NIST AI RMF), responsible AI practices, and data protection controls.
Experience with agentic systems, MCP, enterprise agent platforms, or AI evaluation tooling.
Experience integrating AI into consumer-scale products.

Responsibilities

Lead, hire, develop, and scale AI engineering teams and technical leaders supporting multiple use-case pods and shared platform capabilities (e.g., marketing creative generation, AI product experiences, agentic distribution, enterprise workflow automation), setting clear expectations, providing ongoing coaching and feedback, managing performance, career growth, and leadership development.
Own delivery and engineering quality for LLM-based systems, agents, and integrations, from architecture and build through production operation, cost, latency, and reliability, ensuring teams make sound technical tradeoffs and deliver scalable, maintainable solutions across multiple products and services.
Guide technical direction and lead the development and operation of the CoE’s shared AI platform: evaluation pipelines, behavioral and quality testing, prompt and model management, observability, DLP / sensitive-prompt guardrails, agent frameworks, and reusable AI capabilities that can scale across teams.
Partner with Product, Architecture, and business leaders to translate product and business priorities into technical plans, prioritize engineering work, and balance capacity and resources across teams and initiatives; make informed tradeoffs across delivery commitments and technical investments, including build/buy/partner, vendor, and model decisions.
Establish and maintain effective engineering practices across planning, design and code reviews, on-call, and incident management; proactively manage cross-team dependencies, operational risks, and systemic technical issues, and keep senior stakeholders aligned on key progress, risks, tradeoffs, and decisions.
Ensure every launch meets CoE evaluation, security, and governance standards; collaborate with Legal, Compliance, and Security to move fast with the right guardrails and drive consistent adoption of these practices across AI engineering teams.
Develop, scale, and champion engineering standards, reusable patterns, and playbooks that enable the CoE and partner teams to adopt AI safely and quickly; establish knowledge-sharing practices that scale across teams.
Stay close to the technology through critical architecture, design, and technical decisions, while maintaining primary accountability for engineering leadership and delivery through teams and leaders.
Navigate complex, ambiguous, cross-cutting engineering challenges that span teams and organizational boundaries, establishing scalable approaches and driving alignment where precedent or clear solutions may not exist.