Principal Architect, AI Labs

Salesforce•San Francisco, CA

1d•$197,300 - $401,400

About The Position

AI Labs is seeking a deeply technical, product-savvy AI architect to support Salesforce’s AI Labs portfolio across two stages: evaluating AI technology before acquisition, licensing, or investment, and helping companies accelerate after joining AI Labs. The role provides independent, hands-on technical depth during M&A and investment diligence while helping build shared DeepTech capabilities, models, and infrastructure that portfolio companies can use when doing so creates material leverage. The Principal Architect will advise across the portfolio without becoming a centralized bottleneck. They will work with founders and engineering leaders, assess technology and talent, identify technical risks and opportunities, and communicate recommendations to AI Labs leadership. The position calls for principal- or distinguished-level engineering or architecture experience in AI, ML, and production-scale AI products, with strong technical judgment, product taste, and pragmatic business thinking.

Requirements

Principal- or 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 and post-training, 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- or founder-led engineering teams.
Strong technical judgment, deep product taste, and pragmatic business thinking.
Experience shipping differentiated AI/ML products; the posting emphasizes this over formal diligence experience.

Nice To Haves

Experience with technical diligence, DeepTech investing, acquisitions, or startup evaluation.

Responsibilities

Lead technical and AI diligence for potential acquisitions and strategic investments.
Evaluate architecture, models, data strategy, infrastructure, security, scalability, technical differentiation, and technical debt by testing, studying, using, and remixing technology.
Interview and score DeepTech talent.
Assess build-versus-buy questions and the defensibility of a company’s technology, team, and intellectual property.
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 when adoption creates material leverage.
Identify capabilities that should be built once across AI Labs rather than repeatedly by individual companies, and assess the technical and nontechnical tradeoffs.
Serve as a senior technical advisor across the AI Labs portfolio without becoming a centralized bottleneck.