Senior Software Engineer

Salesforce•Palo Alto, CA

10d•$117,200 - $344,700•Hybrid

About The Position

Salesforce Trust Platform provides foundational security, availability, compliance, and resilience for the Agentic Enterprise. From the hardware-backed Root-of-Trust to application guardrails for administrators, end users, and ISVs, we make every layer of technology secure-by-default. We make it extremely hard to attack Salesforce, misconfigure Salesforce, or introduce security bugs at Salesforce. We scale our zero-trust, always available infrastructure for the most mission-critical enterprise workloads of the world. We multiply our impact with AI. We use agents and automation to achieve auto-containment and time-to-recover targets that are only possible without human-in-the-loop.

Requirements

4+ years of professional Java development experience.
A demonstrated, genuine AI-first approach to engineering, using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in active development workflows.
Ability to design, iterate on, and test system and user prompts that produce structured, parseable LLM outputs (JSON classification, multi-field extraction).
Designing and consuming RESTful APIs alongside working knowledge of AWS (S3, STS, KMS, Lambda, DynamoDB).
Experience with Bazel or Maven and a strong willingness to work in a monorepo.
Experience with Jenkins or equivalent CI/CD pipelines, Docker image creation, and container operations.

Nice To Haves

Experience with TypeScript (Node) and/or Python service development is a strong plus.

Responsibilities

Build and ship high-quality, production-grade software, 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.
Architect and maintain resilient backend services integrating Large Language Models (LLMs) via external APIs, while implementing robust observability frameworks (logs, metrics, traces) to monitor long-running AI jobs.
Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance.
Develop test automation and quality tooling, focusing on unit/CI test generation, test failure analysis, and flake awareness.