Director of Production Support Engineering

Salesforce•Seattle, WA

1d•Hybrid

About The Position

Salesforce is seeking a Director of Production Support Engineering to lead its embedded reliability organization. This role involves managing a team of Lead and Principal Engineers integrated into the product lifecycle, focusing on scaling a high-growth AI product within a complex technical landscape. The position requires defining a roadmap for scaling the commercial Salesforce offering into a multi-region, multi-substrate powerhouse while ensuring the stability of legacy and custom enterprise environments for critical customers. The Director will also architect future offerings like Private Cloud Edition (PCE) and Government Cloud, adhering to strict security and sovereignty requirements. The role operates within an agile team with startup roots, recently acquired and now scaling rapidly.

Requirements

10+ years of experience in Production Engineering, SRE, or Infrastructure roles.
At least 3-5 years in a formal people management capacity.
Highly technical with comfort reviewing architecture designs for Kubernetes, Terraform, and distributed databases (PostgreSQL).
Understanding of the operational nuances of AI/GPU workloads.
Proven track record of managing production environments through a "1 to 100" scaling phase, ideally in a multi-region or multi-cloud context.
Demonstrated ability to articulate a complex technical vision and turn it into an actionable roadmap.
Experience supporting "Big Iron" enterprise customers with custom deployment needs and high-compliance requirements.
Proven ability to lead through major incidents and complex architectural migrations with a calm, decisive approach.
Experience leveraging AI tools to optimize team workflows.
A clear vision for how AI will transform the future of production support.
A demonstrated, genuine AI-first approach to engineering.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.
Advanced prompt engineering skills and the ability to write precise, structured prompts.
Ability to cultivate system context that makes AI outputs reliable, secure, and production-ready.

Nice To Haves

Experience with Government/Sovereign Clouds: Direct experience navigating FedRAMP, IL5/IL6, or similar high-security compliance frameworks.
Salesforce Infrastructure Knowledge: Familiarity with Hyperforce, Salesforce Data Cloud, or the internal mechanics of a "Private Cloud Edition."
Hybrid Infrastructure: Experience managing the bridge between public cloud (AWS/GCP/Azure) and specialized enterprise environments.
Advanced Degree: M.S. in Computer Science or a related technical field.

Responsibilities

Devise and champion a long-term infrastructure strategy that balances rapid AI product innovation with the rigorous demands of enterprise-grade reliability and global scale.
Manage and grow a world-class team of Production Support Engineers, providing technical mentorship, career development, and fostering a high-performance, low-ego culture.
Lead the transition to a multi-region and multi-substrate deployment model within the Salesforce commercial cloud, ensuring the resilience of the "Super Agent" architecture across global geographies.
Oversee the maintenance and support of legacy environments and bespoke customer deployments, ensuring a seamless experience for long-term partners transitioning to the modern stack.
Act as the primary stakeholder for Private Cloud (PCE) and Government Cloud initiatives, ensuring infrastructure meets strict regulatory and data-residency requirements without sacrificing developer velocity.
Define and hold the team accountable to SLIs, SLOs, and error budgets, leading the "incident-to-insight" loop to ensure production issues result in structural improvements.
Collaborate with Product, Engineering, and Security leadership to align infrastructure investments with the broader business goals of the Agentforce ecosystem.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of the development workflow.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows.
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards.
Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance.