AI Labs aims to grow Salesforce into America’s next $1.5T AI mainstay, from ASICs to Agents. We’re looking for deeply technical, product-savvy AI architects who can operate across two critical moments in the deal lifecycle: evaluating AI technology before it is acquired, licensed, or invested in by a larger firm, and accelerating that company after acquisition.

These leaders will provide independent, in-IDE technical depth during M&A or investment diligence, while helping build common DeepTech capabilities, models, and infrastructure that portfolio companies can leverage after joining AI Labs.

What You'll Do

• Lead technical and AI diligence for potential acquisitions and strategic investments. (Formal diligence experience is not required.)
• Critically evaluate (by testing, studying, using, and remixing) architecture, models, data strategy, infrastructure, security, scalability, technical differentiation, and technical debt.
• Interview and score DeepTech talent.
• Assess build-vs-buy questions and the defensibility of a company's underlying technology, team, and IP.
• Partner directly with founders, CTOs, and engineering leaders during diligence.
• Identify material technical risks and opportunities and translate them into clear recommendations for AI Labs leadership.
• Identify opportunities for portfolio companies to adopt shared AI technologies, evaluation frameworks, infrastructure, standards, and services, only where doing so creates material leverage.
• Identify capabilities that should be built once across AI Labs, rather than repeatedly by individual companies, and the technical and nontechnical tradeoffs.
• Serve as a senior technical advisor across the AI Labs portfolio without becoming a centralized bottleneck.

What We're Looking For

• Principal/Distinguished-level engineering or architecture experience in AI (both basic and applied), ML, and production-scale AI products.
• Deep understanding of modern AI architectures, models, training/post-training and inference, data, evaluation, infrastructure, production systems, and how technical choices shape product outcomes.
• Ability to rapidly understand unfamiliar technical stacks and distinguish true technical differentiation from implementation detail.
• Experience working with startup/founder-led engineering teams.
• Strong technical judgment combined with deep product taste and pragmatic business thinking.
• Experience with technical diligence, DeepTech investing, acquisitions, or startup evaluation is nice to have. (Having shipped differentiated AI/ML products matters more.)

Team Values

• Distill
• Always be learning
• Invest in relationships
• Bravery without bravado