Senior Director, Technical Product Management — AI Quality & Evaluation

Salesforce•Seattle, WA

1d•$218,400 - $401,400

About The Position

Salesforce is hiring a senior technical product leader for its Agentforce AI Quality & Evaluation team, an entrepreneurial incubation team that works with AI researchers, applied scientists, and engineers to develop and improve AI products. The role shapes product strategy for AI quality and evaluation, defines how progress is measured, leads customer quality investigations, and delivers a focused feature from early learning through release. It is a hands-on individual-contributor leadership role, with an IC focus expected through 2027. The position leads through product ownership, technical judgment, and cross-functional influence, and includes coaching colleagues. People-management responsibilities may develop as the product and team grow. Engineering partners own technical architecture and production implementation. The successful candidate will help customers achieve credible improvements in real agent workflows. They will use customer evidence and evaluations to guide experiments, product decisions, and releases, while communicating what is known, uncertain, or unproven to customers and stakeholders.

Requirements

A record of building AI/ML products and delivering measurable customer outcomes, with increasing ownership, scope, and impact.
Deep hands-on experience building AI agents and evaluation capabilities in close partnership with AI researchers, applied scientists, or AI engineers. One to two years of substantive generative AI experience may be sufficient when supported by strong AI/ML fundamentals and demonstrated impact.
Strong applied AI/ML judgment across model behavior, prompting, retrieval, agent/tool workflows, and tradeoffs among model, data, system, and product improvements.
Current hands-on ability to inspect data and traces, prototype with coding agents, and reason about experiments, overfitting, training/test splits, edge cases, and uncertainty.
Ability to define quality for non-deterministic systems and challenge conclusions with technical partners.
Principled, flexible thinking: ability to frame customer problems, test assumptions, and change direction when evidence warrants it.
Experience establishing shared standards and resolving cross-team disagreements while preserving accountability and delivery momentum.
Evidence of coaching and mentoring colleagues without relying on reporting authority, and ability to deliver independently in a fast-moving environment.
Clear communication with customers, technical teams, and senior leaders, including honest explanations of what is known, uncertain, or unproven.

Nice To Haves

Experience with evaluation platforms, agent systems, AI observability, retrieval, or enterprise AI products.
Experience building in an AI-native startup or entrepreneurial environment, taking a new product from early customer learning to adoption, and applying lessons from successful and unsuccessful experiments.
Hands-on prototyping with coding agents and delivery of customer-facing performance or outcome experiences.
Experience leading initiatives requiring agreement across research, engineering, product, and customer-facing organizations.

Responsibilities

Set product strategy and roadmap for AI quality and evaluation, connecting customer needs, technical opportunities, and measurable business outcomes.
Establish shared quality definitions, evaluation requirements, coverage priorities, and release criteria with research, engineering, and product partners.
Make investment tradeoffs among customer quality improvements, evaluation capabilities, and product features; align teams on priorities and decision ownership.
Lead customer investigations and experiments, diagnose agent failures, define expected behavior, and partner with researchers and engineers to test changes and validate customer impact.
Build an operating approach that connects customer feedback, evaluation evidence, experiments, releases, and learning across teams.
Partner with researchers, applied scientists, and engineers to design credible evaluations and assess non-deterministic model behavior and grader reliability.
Lead delivery of capabilities that help customers understand and improve AI performance, including prototyping experiences that turn failed interactions into evaluation cases and help customers verify proposed changes.
Coach product partners, junior designers, and technical program managers in problem framing, experimentation, and delivery.
Bring verified competitive insight into strategy and communicate differentiation, limitations, and investment choices to customers and senior stakeholders.