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AI Native Software Engineer

Salesforce · California San Francisco

Location
California San Francisco
Salary
$197,300 - $313,700
Funding
~$184.9B
Posted
Sep 11, 2026

Salesforce is hiring a AI Native Software Engineer based in California San Francisco. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.

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Role details

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Applications will be accepted until 10/05/2026. *Please note for the right person, we would consider a fully remote employee* The Experience: We are looking for an unusual kind of engineer: someone who can reason deeply about data-intensive and distributed systems, then turn that complexity into a product people can understand, trust, and use. You may have built infrastructure, control planes, developer platforms, data systems, or deeply technical enterprise products. You understand that the hardest systems problems are not solved when the backend works. They are solved when users can form an accurate mental model of the system, take powerful actions safely, diagnose what happened, and recover when things go wrong. That challenge is becoming more interesting in an agentic world. Interfaces are no longer just collections of pages and workflows. Users may express intent rather than specify every step; agents may plan and act across complex systems; and the product must make those actions legible, governable, and reversible. We want someone who is excited to rethink what a control plane should be when agents are the primary actors and humans provide direction, oversight, and judgment. This is a hands-on, high-leverage role at the intersection of systems architecture, product engineering, information design, and interaction design. You will help define not only how the system is built, but how its underlying concepts and behavior become a coherent product. Telemetry, visualization, and human judgment: We believe agents will increasingly do the work. The human interface must make it possible to see what was done, understand why it happened, recognize what requires attention, and make consequential judgments with confidence. In this model, telemetry is not a secondary operational concern or a collection of dashboards added after the fact. It is a core part of the user interface. This role will own both control-plane and telemetry experiences: the surfaces through which users express intent, supervise agent activity, understand outcomes, investigate anomalies, and intervene when human judgment adds value. That requires more than displaying data. It requires choosing the right abstractions, preserving context and provenance, revealing causality where possible, and turning dense system behavior into information that people can grasp and act on. We are looking for someone serious about the craft of information design—familiar with Edward Tufte’s work and informed by thinkers such as Bret Victor, Ben Shneiderman, or Tamara Munzner—without being doctrinaire about any one approach. You should know how to use hierarchy, comparison, annotation, small multiples, progressive disclosure, and thoughtful visual density to make complex data genuinely understandable. You should also have a strong point of view about the boundary between human and machine. Human-in-the-loop should be intentional, not a reflexive approval step placed in every workflow. Some decisions require human review before action; others are better served by clear policies, strong guardrails, complete auditability, and precise escalation when something falls outside expectations. You will help determine which model is appropriate, and design the evidence and interactions that make each model trustworthy. What you’ll do: Own and build core control-plane and telemetry experiences, from underlying domain models, events, and APIs through the user-facing product. Translate distributed-system concepts—state, dependencies, policy, identity, lineage, orchestration, failure, and recovery—into clear product primitives and interactions. Develop workflows for agent-executed work, including how human intent is expressed, permissions are enforced, activity is observed, outcomes are explained, and changes are audited or reversed. Create information-rich visualizations that help users understand state, change over time, relationships, anomalies, causality, and uncertainty without flattening meaningful complexity. Decide thoughtfully what should be done by an agent, what requires human judgment, and what belongs in a graphical interface, API, or programmable surface. Define patterns for human oversight, including when to require review before action, when to escalate exceptions, and when durable auditability is more valuable than synchronous approval. Prototype new interaction models, test them against real technical constraints, and carry the strongest ideas into durable production systems. Shape architecture and product direction through working software, clear technical judgment, and a strong point of view about usability. You're Our Person If: Deep experience with data-intensive or distributed systems. You reason naturally about state, consistency, failure modes, asynchronous behavior, scale, and operational tradeoffs. Experience building platform or control-plane software—not only consuming infrastructure, but creating the systems through which other people understand and operate it. Experience directing multi-agent software development workflows and applying AI-native engineering practices, including specification-driven planning, implementat

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