Software Architect - AI-Native Developer Platform (Salesforce DX)

Salesforce•San Francisco, CA

1d•Hybrid

About The Position

Salesforce is seeking an exceptional Architect with deep engineering experience to help define and execute the long-term technical vision for Salesforce DX. This role involves designing and building high-throughput systems, writing production-quality code, prototyping frontier concepts, and establishing how AI agents safely modify enterprise software at scale. The Architect will partner across engineering leadership, Distinguished Engineers, product management, AI research teams, and platform organizations to solve complex technical problems related to developer productivity, AI agents, enterprise trust, and software lifecycle automation. The Salesforce Developer Experience (DX) organization is building the future of enterprise software development, aiming to make Salesforce the easiest enterprise platform to build on for both human developers and autonomous AI agents. They are transforming traditional developer tooling into an AI-native development platform that spans IDEs, intelligent coding agents, CLI, APIs, DevOps, enterprise governance, evaluation frameworks, and cloud-native infrastructure. Products include Agentforce Vibes, DX Workspaces, Salesforce CLI, DevOps Center, platform APIs, developer services, and foundational capabilities powering millions of developers and the next generation of AI-assisted software delivery.

Requirements

15+ Years of Systems Engineering: Proven track record of designing, building, and operating production-scale distributed systems and cloud infrastructure.
Hands-On Builder Mindset: High proficiency in languages such as Java, Go, TypeScript, or Python. A track record of taking ambiguous concepts directly into production.
Salesforce Architecture Depth: Practical understanding of Salesforce metadata architectures, deployment models, and core runtime frameworks (Apex, LWC).
Agentic Systems Expertise: Hands-on experience with LLMs, AI agents, retrieval systems, tool calling, MCP ecosystems, evaluation frameworks, and multi-agent orchestration platforms.
Enterprise Security & Governance: Experience implementing sandboxing, static analysis, policy enforcement, compliance, and observability for code-generation pipelines.
Technical Leadership & Influence: Demonstrated ability to drive cross-functional technical initiatives, build consensus without direct authority, and communicate effectively with executives and engineers alike.

Nice To Haves

Experience contributing to open-source developer tooling, CLI engines, or public API ecosystems.
Background in developing agent evaluation frameworks or automated CI/CD code generation pipelines.

Responsibilities

Define AI-Native Developer Architecture & Multi-Agent Orchestration: Lead the multi-year architectural evolution of Salesforce’s developer platform from traditional human-in-the-loop tooling to autonomous software engineering ecosystems. Architect stateful, multi-agent orchestration engines capable of decomposing complex features into executable engineering tasks, generating code, handling dependencies, and self-correcting via runtime feedback loops. Define standardized interaction protocols (such as Model Context Protocol / MCP) and schema contracts to allow third-party AI agents, internal platform tools, and external IDE extensions to seamlessly interoperate with Salesforce services.
Deep Integration with Salesforce Runtime & Metadata Ecosystem: Bridge modern LLM capabilities with Salesforce architectures, designing abstractions that allow agents to reason over complex org metadata, dependency graphs, Apex execution models, and LWC component hierarchies. Build real-time, high-precision retrieval architectures (RAG/graph-based code indexing) that provide agents with full semantic awareness of multi-million-line enterprise orgs, including custom fields, business logic, security permissions, and schema variations. Redefine developer interfaces (Salesforce CLI, DX Workspaces, Agentforce Vibes) so human engineers can delegate, monitor, review, and collaborate with autonomous agents within unified workflows.
Build Enterprise Trust, Security & Execution Guardrails: Design isolated, ephemeral execution sandboxes capable of safely executing AI-generated code, running automated test suites, and performing dynamic analysis without risk to production metadata or enterprise data boundaries. Establish deterministic evaluation and safety frameworks to validate agent output—enforcing security compliance, static code analysis, unit test coverage, zero-trust access control, and governance rules before code merge. Implement fine-grained observability, audit logging, and provenance tracking for agentic modifications to ensure enterprise compliance and complete traceability of machine-generated code.
Hands-On Building, Prototyping & System Optimization: Remain directly hands-on: write high-throughput production code (in languages like Java, Go, TypeScript, or Python), author core SDKs, and build functional prototypes for next-generation agent workflows. Lead high-stakes performance engineering initiatives to minimize agent context latency, optimize vector/graph search performance over large codebases, and maintain sub-second API responsiveness across distributed cloud systems.
Cross-Organizational Strategy & Technical Leadership: Drive unified technical direction across core engineering groups—including AI Platform, Agentforce, Core Platform, Security, Trust, DevOps, and Runtime Infrastructure. Mentor senior architects and principal engineers across the organization, elevating engineering quality, establishing design review standards for AI systems, and fostering a culture of pragmatic innovation.