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Responsibilities
- Defines the technical direction for model maintenance (retraining cadences, drift mitigation, performance recovery) and evolution (new capabilities, architecture improvements, multi-modal expansion). Translates cross-product performance patterns into investment recommendations for evaluation leads
- Provides cross-product context, defines what good looks like at the model level, and informs evaluation methodology. Evals owners own execution of verification pipelines within their products; this role ensures consistency and identifies gaps across the portfolio while building institutional competence by surfacing performance patterns and proven methodologies, enabling evals captains' ability to execute and unblocking them as needed
- Defines what leadership needs to see, how model health should be measured and reported, and what thresholds trigger escalation
- Provides thought partnership to evals managers on narrative of model health, provides visibility into our classification strategy and accuracy measurement process
- Works with evaluation managers to drive cross-app taxonomy alignment in alignment with cross-functional needs and advises on a strategy for the migration of LLM accuracy assessment to judges
- Owns the consolidated view of all production model performance, identifies systemic patterns and emerging risks, and ensures leadership can verify model health on demand
- Partners with AI Implementations, operational systems teams and the Metrics & Measurement team to build and maintain the infrastructure that surfaces this information
- Establishes performance guardrails that evals captains implement. Continuously scans industry developments and best practices to incorporate into org-wide approach
- Maintains a tight feedback loop with product and eng teams across apps to ensure alignment on production priorities and deployment risks
- Deploys deep SME expertise to diagnose, unblock and directly resolve technical bottlenecks to complex model quality problems (atrophy, accuracy regressions, performance plateaus) when evaluation leads encounter blockers they cannot resolve independently
- Drives alignment with cross-functional teams (quality and reliability partner teams) on tooling needs to support Product Operations classification strategy (ML classification tooling for initial-tier classification, user voice, breakdown graphs). Advocates for investment, flags risks, influences direction
Minimum Qualifications
- Bachelor's degree in a directly related field, or equivalent practical experience
- 7+ years of experience in strategy, operations, consulting, or data analysis
- Analytical experience using data to tell a story and influence product direction using intermediate to advanced SQL
- Experience building or deploying AI/ML solutions, LLM model quality or automation in production workflows
- Strong communication skills with ability to influence multiple cross-functional stakeholders and senior leadership
- Experience breaking down ambiguous issues into component parts to develop solutions
- Ability to design AI workflows that operate effectively within enterprise data sensitivity constraints, balancing quality and privacy principles
Preferred Qualifications
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience operating in flat, IC-heavy org structures with high individual autonomy
- Demonstrated history of evaluating industry best practices and providing organizational recommendations on approaches to AI models and development
- Experience in product quality, QA, or technical program management
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Familiarity with LLMs, AI agents, or ML evaluation frameworks
- Experience working with global/remote teams
$123,000/year to $179,000/year + bonus + equity + benefits
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