Staff Software Engineer, Labs: Applied AI

Anthropic•San Francisco, CA

14h•$320,000 - $405,000•Hybrid

About The Position

At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Applied AI is one of the newest explorations within Anthropic Labs, the internal accelerator behind Claude Code, MCP, and Claude Design. Most of the world's work happens far from a code editor, and the people doing it have barely begun to feel what frontier AI can do. We believe Claude has a transformative role to play here — and we're at the earliest stage of exploring what that could look like. The engineers who join now will define where it goes. We're looking for versatile, entrepreneurial engineers who are energized by building for users unlike themselves. In this role, you'll take frontier AI capabilities and turn them into applications that professionals in less software-native roles can pick up and trust — rapidly building and testing new experiences, partnering directly with researchers, domain experts, and users, and generating the insights that shape where this exploration goes next. You'll need to be comfortable with ambiguity, willing to kill your own projects when the data says to, and energized by the pace of building in uncharted territory.

Requirements

8+ years of experience building full-stack applications, with a track record of zero-to-one work in startup or startup-like environments
Deeply curious about how other industries work, and enjoy translating messy, real-world workflows into simple software
Thrive in ambiguity and are energized (not anxious) by uncertainty — you're comfortable working on projects that might not exist in three months
Have a hacker mentality: high agency, bias toward shipping, comfort with technical debt when it's the right tradeoff
Deeply user-centric — you validate ideas with actual users before over-investing and talk about problems before solutions
Can articulate learnings from failed or killed projects without defensiveness; you treat your work as experiments
Hold strong opinions loosely — you advocate forcefully for ideas but change your mind based on evidence
Are a generalist who can transition between different problem spaces as priorities shift
Work independently with good judgment about what matters, without needing constant direction
Communicate effectively and can make complex AI capabilities feel intuitive to people who don't think in software
Care about the societal impacts and ethics of your work

Nice To Haves

Experience building products for industries outside of tech — e.g., healthcare, manufacturing, logistics, construction, energy, agriculture, financial services, education, or the public sector
A previous career, or deep hands-on exposure, in a field outside of software — you've been the user these products serve
Background conducting embedded or field-based discovery: user research, interviews, ride-alongs, and usability testing with frontline professionals
Experience integrating with the systems these industries actually run on (ERPs, EHRs, CRMs, dispatch, scheduling, or point-of-sale systems)
Experience shipping software or AI applications to non-technical or frontline users — you know how to design for people who will never read documentation, and you measure success by real-world adoption rather than technical elegance
Hands-on applied AI experience — you've built and deployed products powered by AI/ML or large language models
Experience collaborating directly with research teams in AI/ML environments

Responsibilities

Rapidly prototype full-stack applications that bring frontier AI into workflows that have never been software-first, shipping early and often to maximize learning
Immerse yourself in unfamiliar domains: sit with users, learn how their work actually gets done, and encode that understanding into products, evaluations, and workflows
Collaborate closely with research teams to understand new model capabilities and translate them into tools that non-technical professionals reach for first
Work directly with internal teams and external partners across industries to gather feedback, iterate quickly, and validate (or invalidate) product concepts
Design and run structured experiments to test hypotheses, balancing creative exploration with rigorous evaluation
Generate documentation and insights to guide successful prototypes toward full product teams
Provide feedback to research teams about model effectiveness in real-world, domain-heavy settings and where capabilities can improve
Flexibly contribute across Labs initiatives based on organizational priorities and emerging opportunities — context from one project should inform the next