Value Acceleration Engineer MTS

Salesforce•San Francisco, CA

1d•$117,200 - $194,200•Hybrid

About The Position

Salesforce is seeking a Value Acceleration Engineer to join their AI Labs team. This role focuses on building Systems Integration Agent, which learns to operate enterprise applications like SAP, Oracle, and Workday, and turns that knowledge into reusable, code-backed actions. The team operates like a startup within Salesforce AI Labs, offering autonomy to shape an early product with the resources of Salesforce. The Value Acceleration Engineer will write code, build with customers, and get real workflows into production, with learnings from deployments influencing future product development.

Requirements

Can write and debug JavaScript or TypeScript and reason about web applications, APIs, and data flow.
Enjoys working directly with users: asking precise questions, building alongside them, and explaining technical problems clearly.
Has built working software and can explain implementation choices, tests, and debugging.
2+ years of relevant experience.

Nice To Haves

Playwright, browser automation, browser extensions, RPA, or enterprise integration.
Agent systems, tool execution, evaluations, or failure recovery.
SAP, Oracle, Workday, Salesforce, or similarly complex enterprise applications.
Forward-deployed engineering or customer-facing product development.

Responsibilities

Work with process owners and customer IT to select workflows, define success, and secure the test access and data needed to build.
Run hands-on sessions that help customers use the product themselves, with clear scope and readiness expectations.
Help customers understand where the product can deliver value, explain its capabilities and limitations clearly, and connect technical results to business outcomes.
Write custom JavaScript tools when existing capabilities cannot complete a task.
Debug failures across browser behavior, agent execution, application state, and backend services.
Build tests for business outcomes, exceptions, partial completion, and recovery.
Help launch and stabilize workflows, documenting monitoring and recovery procedures and establishing support ownership with the team.
Turn deployment lessons into reusable tools and tests, and work with core engineering to address recurring product gaps.
Use AI development tools thoughtfully while remaining accountable for code quality, security, and maintainability.