Senior Director, Technical Product Management — AI Quality & Evaluation

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a technical product leader for Agentforce, an entrepreneurial incubation team that combines startup pace and ownership with access to enterprise customers and Salesforce’s distribution. The role shapes product strategy for AI quality and evaluation, defines how progress is measured, leads a customer quality workstream, and delivers a focused product feature from early learning through release. This is a hands-on individual-contributor leadership position, expected to remain IC-focused through 2027. The leader works closely with AI researchers, applied scientists, engineers, product partners, and customers, using coding agents and experimentation to move quickly. Engineering partners own technical architecture and production implementation. The role also coaches colleagues, including junior designers and technical program managers; people-management responsibilities may develop as the product and team grow. The position is accountable for connecting customer needs, technical opportunities, and measurable business outcomes. It establishes shared quality definitions, evaluation requirements, coverage priorities, and release criteria; makes investment tradeoffs; and creates an operating approach linking customer feedback, evaluations, experiments, releases, and learning. The role personally investigates agent failures, defines expected behavior, partners with technical teams to test changes, and validates customer impact. It also leads capabilities that help customers understand and improve AI performance, and communicates competitive insight, product differentiation, limitations, and investment choices to customers and senior stakeholders. Success means customers see credible improvements in real agent workflows, supported by trustworthy evaluations. The role independently takes an initial quality problem from failure analysis through experimentation and customer validation, delivers a focused feature with engineering, and improves team release and investment decisions through evidence, product judgment, and coaching.

Requirements

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.
Substantive generative AI experience; one to two years can be sufficient when supported by strong AI/ML fundamentals and demonstrated impact.
Strong applied AI/ML judgment across model behavior, prompting, retrieval, agent and 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: frame customer problems, test assumptions, and change direction when evidence warrants it.
Experience establishing shared standards and resolving cross-team disagreements while maintaining 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 distinguishing 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 and taking a new product from early customer learning to adoption, incorporating 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, including analyzing incorrect agent answers or failed tool-driven workflows, defining expected behavior, and validating changes with researchers and engineers.
Build an operating approach that connects customer feedback, evaluation evidence, experiments, releases, and cross-team learning.
Partner with researchers, applied scientists, and engineers to design credible evaluations and assess model behavior and grader reliability.
Lead delivery of customer-facing capabilities that help customers understand and improve AI performance, such as turning failed interactions into evaluation cases and verifying proposed changes.
Coach product partners, junior designers, and technical program managers in problem framing, experimentation, and delivery.
Bring verified competitive insight into strategy and clearly communicate differentiation, limitations, and investment choices to customers and senior stakeholders.