Software Engineering SMTS

Salesforce•San Francisco, CA

3d•Remote

About The Position

Salesforce AI Labs is building a product that learns from human demonstrations and turns complex workflows into repeatable automation across enterprise applications. This role involves addressing hard engineering questions related to agent success, reasoning, recovery, and asking for help. The team works directly with customers to build alongside them, turning discoveries into reusable product capabilities. The individual will own substantial systems from design through deployment and help take a new offering from early customer adoption through general availability, backed by Salesforce’s scale and reach.

Requirements

Independently owned a substantial backend service or system through design, deployment, production failures, and subsequent improvements.
Hands-on experience building and evaluating LLM-powered systems, and can explain how you assessed their quality and failure modes.
Can reason through concurrency, persistent state, retries, and partial failures in systems that perform consequential actions.
Proficient in Python, Go, Java, or JavaScript/TypeScript.
Can turn an unclear customer problem into a bounded technical approach, test it with users, and carry it through to production.
Want direct customer engagement to be part of your engineering work, including investigating problems and discussing technical tradeoffs.
Use AI development tools thoughtfully and take responsibility for the correctness, security, and maintainability of what you ship.

Nice To Haves

Agent tool use, code generation, automated recovery, or evaluation frameworks.
Browser automation, workflow orchestration, or enterprise integrations.
Building an early-stage product or working in a customer-facing engineering role.

Responsibilities

Drive Agent Development & Architecture: Build systems that learn from demonstrations and combine AI reasoning with deterministic execution to complete workflows reliably.
Develop recovery and verification capabilities so agents can adapt to changes, confirm outcomes, and recognize when human intervention is needed.
Build backend services with robust workflow state management, failure handling, and secure access to customer environments.
Create evaluations and diagnostics that measure task success, expose regressions, and make failures understandable.
Build Directly With Customers: Work alongside customers to understand their workflows, prototype solutions, and validate results in their environments.
Investigate where automation succeeds or fails, then turn those findings into improvements to the core product.
Help customers move from their first successful workflow toward repeatable, increasingly self-service adoption.
Partner with forward-deployed engineers and product management to decide which customer needs should shape the roadmap.
Shape Technical Direction & Grow the Team: Own technical decisions for substantial components, from initial design through production operation.
Evaluate model selection, tool use, and context management against reliability, latency, and cost.
Mentor peers through design discussions and code reviews, and participate in hiring.