# Staff Software Engineer, Labs: Applied AI

**Company:** [Anthropic](https://hotfix.jobs/companies/anthropic)
**Location:** San Francisco, CA, New York, NY
**Role:** Fullstack Engineering
**Salary:** $320k – $405k/yr
**Experience:** 8+ years
**Skills:** Full-Stack Development, Prototyping, User Research, Ai/Ml Integration, LLMs, ERP, Ehr, CRM, Python, JavaScript
**Posted:** 2026-07-08

> Build and prototype full-stack AI applications for non-technical professionals in diverse industries. Requires 8+ years full-stack experience, strong user-centric mindset, comfort with ambiguity, and ability to translate frontier AI capabilities into trusted real-world tools.

## Job Description

## 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

## 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
- 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
- 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
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role

## 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

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