Field AI Engineer

At JFrog, we’re reinventing DevOps to help the world’s greatest companies innovate -- and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit and just all-around great people. Here, if you’re willing to do more, your career can take off. And since software plays a central role in everyone’s lives, you’ll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production -- a concept we call “liquid software.” Wouldn't it be amazing if you could join us in our journey? Most engineers sit behind a desk. This one doesn't. As our Field AI Engineer , you'll be embedded in the San Francisco AI ecosystem: attending meetups, demos, hackathons, and industry events where practitioners are doing interesting work. You'll need technical depth to hold substantive conversations with researchers and engineers, and enough curiosity to consistently come away having learned something useful. This role is modeled on the Forward Deployed Engineer archetype pioneered by companies like OpenAI and Anthropic, but with a twist. Where traditional FDEs embed with a single customer, you embed with the entire ecosystem . Your customer is the SF AI community. Your deliverable is intelligence. You'll represent our company as a peer, not a pitch. Everything you learn (the tools gaining traction, the patterns that are working, the problems enterprises keep running into) feeds directly back into our product, strategy, and roadmap. As a Field AI Engineer in JFrog you will... Field Intelligence & Ecosystem and Community Scanning Attend AI meetups, research demos, hackathons, and conferences across the SF Bay Area as a consistent, trusted presence Build a structured view of the AI vendor and startup landscape — what's emerging, what's enterprise-ready, what's overhyped Run a regular intelligence loop: weekly signal briefs, monthly deep-dives for leadership, and ad hoc alerts when something important surfaces Identify and track tools, frameworks, and architectural patterns gaining real adoption before they become mainstream Deep Community Relationships Build authentic relationships with AI researchers, startup founders, enterprise architects, and practitioners — as a peer, not a vendor Become a known and trusted voice in the SF AI scene; someone people want to loop in, not avoid Maintain a living network map of who's building what, who's thinking about what problems, and where the interesting work is happening Technical Credibility & Hands-On Evaluation Get hands-on with new tools, APIs, and agentic frameworks — prototype and evaluate them firsthand before forming a view Engage credibly in deep technical conversations about LLMs, RAG architectures, agentic systems, fine-tuning, prompt engineering, and enterprise AI infrastructure Produce concise technical evaluations: what a tool does, how it works, where it breaks, whether it matters for enterprise Internal Amplification Act as the connective tissue between the external AI world and our internal teams — product, engineering, and leadership Run regular internal "what's new in AI" sessions to keep the team sharp and ahead of the curve Partner with product to ensure field learnings shape our roadmap; partner with GTM to sharpen competitive positioning Occasionally write or speak externally, not on a content treadmill, but when you have something genuinely worth saying To be a Field AI Engineer in JFrog you need... Technically deep: you've built real things with AI. You can evaluate a new tool in an afternoon, spot architectural tradeoffs immediately, and hold your own in any technical conversation Genuinely embedded: you're already showing up at SF AI events, or you will be within 30 days of starting. You find energy in these rooms, not obligation A strong synthesizer: you can take a noisy week of conversations and distill it into three things that actually matter Enterprise-aware: you understand what it means to deploy AI inside a large organization: procurement, security, integration complexity, change management, organizational politics Inbound-first: you're more interested in what you can learn from a conversation than what you can say in it Love bringing the signal back: you enjoy translating external learnings into insights the organization can act on Qualifications 4+ years in a technical hands-on role: engineering, ML, solutions engineering, forward-deployed engineering, or similar Hands-on experience with modern AI/ML tooling: LLMs, RAG pipelines, agentic frameworks (LangChain, LlamaIndex, AutoGen, or equivalent) Strong Python skills; comfort with APIs, cloud infrastructure, and developer tooling Demonstrated presence in the AI community — meetups, open-source, writing, speaking, any of it Based in San Francisco or willing t