At Anthropic, the Applied AI Labs team is building frontier AI capabilities into applications for professional users who are not software‑native. This Staff Software Engineer will prototype full‑stack products, collaborate with research teams, iterate rapidly with users and partners, and shape the next generation of AI‑powered workflows.
Staff Software Engineer, Labs: Applied AI at Anthropic
On-site - San Francisco, CA, United States
More jobs at AnthropicSalary
USD 320,000 - 405,000
Requirements
Skills
- 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
- Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience
- 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
Technologies
full-stack developmentAI/MLlarge language modelsERPsEHRsCRMsdispatch systemsscheduling systemspoint-of-sale systems
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