Principal Software Engineer, AI

<p> <strong>Headquarters:</strong> United States, Remote </p> <p><span><strong>Job Title:</strong> Principal Software Engineer, AI&nbsp;</span></p> <p><span><strong>Location</strong>: Remote - US</span></p> <p>Salesloft is building the next era of enterprise revenue — one where teams make confident decisions powered by AI and real signals. By combining our scale, insights, and AI innovation, we're building the industry's first Predictive Revenue System, enabling humans and AI to work together to make smarter decisions and drive consistent growth.</p> <p>With thousands of customers using our platforms every day, we have an unmatched view into how revenue is actually won — the Revenue Context that reveals what happens, when, and with what outcome. This gives us a unique opportunity to transform an entire category and set a new benchmark for how modern revenue teams operate.</p> <p>Join us to help transform how companies around the world run revenue — and build the platform that will guide leading revenue teams into the future.</p> <p><strong>THE OPPORTUNITY</strong></p> <p>At Salesloft, our Principal Engineer for AI sets the technical direction for how machine intelligence works across our Predictive Revenue System. This is the most senior individual-contributor role on our AI engineering team: you will architect the systems that turn LLMs, machine learning models, and agentic workflows into production capabilities that thousands of enterprise revenue teams depend on.</p> <p>This is deep, ambiguous, frontier work. You will make the foundational architectural decisions for AI at Salesloft — how we serve models at scale, how we build reliable systems on top of non-deterministic components, how we evaluate and guardrail agentic behavior, and how we keep pace as the underlying foundation models evolve. You will set the standards other engineers build on and act as a technical multiplier across the AI organization.</p> <p>On a day-to-day basis, you will:</p> <ul> <li>Set the technical architecture and direction for AI systems across the platform — model serving, LLM-powered features, agentic workflows, and the infrastructure beneath them.</li> <li>Design production-grade systems that are reliable and observable despite being built on non-deterministic, probabilistic AI components.</li> <li>Architect evaluation frameworks, guardrails, and quality systems that make AI behavior safe, measurable, and trustworthy at enterprise scale.</li> <li>Lead the most complex AI engineering problems end to end — from research-adjacent prototyping through hardened production delivery.</li> <li>Establish engineering standards and best practices for building AI-native systems, and mentor senior engineers across the org.&l