Senior/Lead Software Engineer, Developer Productivity (AI Tooling)

Salesforce•San Francisco, CA

6d•$148,500 - $285,800•Hybrid

About The Position

The AI Solutions team in Technology & Product builds internal products and platforms that help Salesforce engineers adopt AI-assisted development safely, effectively, and measurably. Our work spans developer experiences, AI usage and outcome intelligence, governance, telemetry, and integrations with coding tools and engineering systems. As a Lead Member of Technical Staff, you will design, build, and own full-stack product and platform capabilities across desktop applications, backend services, data pipelines, dashboards, and developer-facing integrations. You will help turn fast-moving AI developer productivity ideas into reliable software that thousands of Salesforce engineers can use every day.

Requirements

6+ years of experience designing and building production software, including full-stack applications, backend services, developer tools, or internal platforms.
Strong programming skills and experience working across frontend, backend, APIs, data stores, and distributed system boundaries.
Experience taking ambiguous product or platform requirements from design through implementation, testing, rollout, and production ownership.
Ability to explain initiatives and designs clearly to engineers, product partners, and engineering leadership; make sound technical tradeoffs; influence stakeholders; and lead complex work across teams.
A demonstrated, genuine AI-first approach to engineering — using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.
A related technical degree is required.

Nice To Haves

Experience building developer tools, AI coding tool integrations, internal platforms, telemetry systems, or enterprise desktop applications.
Familiarity with React, TypeScript, Go, service APIs, event pipelines, analytics systems, or cloud and Kubernetes-based services.
Experience building self-service experiences, dashboards, plugin systems, command-line tools, or standard integration patterns for engineering teams.
Interest in AI developer productivity and helping engineering teams adopt AI tools in ways that are measurable, trustworthy, and scalable.

Responsibilities

Lead the technical design and delivery of complex, cross-cutting capabilities across internal developer-productivity products, spanning desktop experiences, backend services, data and analytics platforms, governance workflows, and integrations with AI coding tools and engineering systems.
Build backend APIs, data models, ingestion flows, dashboards, and user experiences that connect AI tool usage to outcomes engineers and leaders can act on.
Set technical direction for a product or platform area, translating ambiguous requirements into design specifications, test strategies, and incremental delivery plans; partner with product and engineering stakeholders to shape medium-term priorities and validate feasibility.
Define and raise the operational bar across the systems you lead by establishing telemetry, SLIs and SLOs, controlled rollout strategies, playbooks, and long-term ownership practices; lead incident investigation and root-cause analysis when needed.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Establish and curate the shared system context - designs, constraints, standards, operational knowledge, and decision records - that helps both engineers and AI agents operate accurately and reliably.
Raise the team's technical quality through design and code reviews, coaching, pairing, and clear definitions of done; delegate appropriately while remaining accountable for the overall technical outcome.