Principal AI Solutions Engineer - Salesforce AI Labs

The opportunity

Salesforce AI Labs brings together AI-native businesses and software companies evolving toward agentic business models. Each has distinctive strengths, customers, and a focused go-to-market strategy. Our opportunity is to connect these capabilities into new ways for customers to operate, respond, and grow.

We are building a small team of hands-on technology builders who can turn that opportunity into working software and compelling business stories. You will connect products, models, agents, and data into demonstrable solutions—and help customers and executives understand their value.

This is a senior individual-contributor role combining software engineering, applied AI, solution architecture, and storytelling. You will move comfortably from a customer problem to code, from an API integration to a live demonstration, and from technical detail to an executive conversation.

Your mission

Build and demonstrate how capabilities across the Salesforce AI Labs portfolio can work together to deliver measurable customer outcomes faster.

You will show what customers can achieve with current capabilities, explore what becomes possible through new combinations, and make the path between the two clear.

What you will do

• Build connected solutions. Create working prototypes, API integrations, and agent workflows spanning multiple products and enterprise systems.
• Start with the business problem. Work with customers, portfolio leaders, and go-to-market teams to identify use cases where combining capabilities produces meaningful additional value.
• Create compelling demonstrations. Develop reliable, repeatable experiences that make complex technology understandable and show a clear connection between customer needs, system actions, and business results.
• Tell the story at every level. Present to executives, customers, developers, and sales teams, adapting the depth while preserving technical credibility.
• Explore the next opportunity. Use rapid experimentation to test new agentic experiences and architectures. Clearly distinguish available capabilities, working prototypes, and future concepts.
• Make the work reusable. Package reference architectures, integration components, demo environments, and playbooks that other teams can adopt.
• Measure value and speed. Establish baselines and evaluate outcomes such as implementation time, process cycle time, cost to serve, service resolution, and customer experience.
• Connect learning to product decisions. Surface integration gaps, customer demand, and technical constraints to product and engineering leaders, with evidence from what you build.

What you bring

• A track record of personally building customer-facing software, integrations, or technical prototypes that solve real business problems.
• Strong coding skills in Python, TypeScript/JavaScript, or comparable languages, with the ability to build and debug across services and a usable demonstration interface.
• Fluency in modern API architectures, including authentication, webhooks, event-driven integration, data contracts, and reliable execution across systems.
• Practical experience building with models and tools from providers such as OpenAI and Anthropic, alongside relevant open-source agent frameworks and harnesses.
• Understanding of tool calling, retrieval, orchestration, context management, evaluations, observability, and human approval within agent workflows.
• Sound judgment about model limitations, reliability, latency, cost, data access, and the requirements for moving from prototype to customer deployment.
• Strong storytelling and presentation skills, including the ability to deliver a live demonstration and explain its business significance.
• The initiative to work through ambiguity, learn unfamiliar products quickly, and ship useful iterations with a small team.
• The ability to turn a broad ambition into a testable use case with a credible path to customer value.

Experience that would be especially useful

• Forward-deployed engineering, applied AI engineering, solutions engineering, or customer-facing solution architecture.
• Building demonstrations or proofs of value involving multiple enterprise products.
• Process intelligence, workflow automation, customer research, customer service, or digital experience platforms.
• Creating reusable technical assets that support several customers or go-to-market teams.
• Startup or founding-engineer experience with direct customer engagement.

What success could look like in the first 90 days

These are proposed initial outcomes, to be calibrated to product access and team capacity:

• First 30 days: Understand the portfolio, map integration opportunities, and select two high-value customer scenarios with business sponsors and measurable outcomes.
• By 60 days: Deliver one working demonstration across multiple products, with a clear story, documented dependencies, and an initial evaluation of reliability and value.
• By 90 days: Validate the scenario with customer or field teams; package it for reuse; and propose the next build priorities based on evidence.