[Hiring] Senior Director, Technical Product Management @Salesforce

Role Description

Help build the next generation of Agentforce. We’re an entrepreneurial incubation team within Salesforce, combining the pace and ownership of a startup with access to enterprise customers and Salesforce’s distribution. We’re looking for a technical product leader with deep AI expertise who wants to turn frontier capabilities into products customers can rely on.

Our team works closely with AI researchers, applied scientists, and engineers, uses coding agents extensively, and builds and experiments outside the core platform to iterate quickly. You’ll shape product strategy for AI quality and evaluation, establish how we measure progress, and personally lead a customer quality workstream and a focused feature from early learning through delivery.

This is a hands-on individual contributor leadership role, with an IC focus expected through 2027. You’ll lead through product ownership, technical judgment, and cross-functional influence while coaching colleagues, including junior designers and technical program managers. People-management responsibilities may develop as the product and team grow. You should enjoy inspecting traces and data, challenging evaluation results, prototyping with coding agents, and working directly with customers. Engineering partners own technical architecture and production implementation.

Core Responsibilities & Scope

• Set a coherent 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 explicit investment tradeoffs across customer quality improvements, evaluation capabilities, and product features; align teams on priorities and decision ownership.
• Personally lead customer investigations and experiments, defining expected behavior and partnering 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, applying AI/ML fundamentals to assess non-deterministic model behavior and grader reliability.
• Lead delivery of capabilities that help customers understand and improve AI performance, such as prototyping experiences that clarify quality gaps.
• Coach product partners, junior designers, and technical program managers in problem framing, experimentation, and delivery; raise the quality of decisions through mentorship and practical example.
• Bring verified competitive insight into strategy and communicate differentiation, limitations, and investment choices clearly to customers and senior stakeholders.

Qualifications

• 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.
• Strong applied AI/ML judgment across model behavior, prompting, retrieval, agent/tool workflows, and the 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.
• Principled, flexible thinking: frame the underlying customer problem, test assumptions, and change direction when evidence warrants it.
• Experience establishing shared standards and resolving cross-team disagreements while preserving clear accountability and delivery momentum.
• Evidence of coaching and mentoring colleagues without relying on reporting authority, and the ability to deliver independently in a fast-moving environment.
• Clear communication with customers, technical teams, and senior leaders, including an honest account of what is known, uncertain, or still unproven.

Preferred Qualifications

• Evaluation platforms, agent systems, AI observability, retrieval, or enterprise AI products.
• Building in an AI-native startup or entrepreneurial environment, taking a new product from early customer learning to adoption.
• Hands-on prototyping with coding agents and delivery of customer-facing performance or outcome experiences.
• Leading initiatives that require agreement across research, engineering, product, and customer-facing organizations.

What success looks like

Customers see credible improvement in real agent workflows, supported by evaluations they can trust. You independently take an initial quality problem from failure analysis through experimentation and customer validation, and deliver a focused feature with engineering. Teams make better release and investment decisions because of the evidence, product judgment, and coaching you bring.

Benefits

• Time off programs
• Medical, dental, vision, mental health support
• Paid parental leave
• Life and disability insurance
• 401(k)
• Employee stock purchasing program