Staff Software Engineer, Labs: Applied AI

About the role

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

Responsibilities

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

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

• Have 8+ years of experience building full-stack applications, with a track record of zero-to-one work in startup or startup-like environments
• Be 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
• Be a hacker mentality: high agency, bias toward shipping, comfort with technical debt when it's the right tradeoff
• Be deeply user-centric — you validate ideas with actual users before over-investing and talk about problems before solutions
• Generate documentation and insights to guide successful prototypes toward full product teams
• 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

Qualifications

• Strong candidates may also have 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
• Experience 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

Skills

• Strong opinions loosely held
• Comfortable working on projects that might not exist in three months
• Ability to articulate learnings from failed or killed projects without defensiveness
• Flexibility to contribute across Labs initiatives based on organizational priorities and emerging opportunities
• Ability to communicate effectively and make complex AI capabilities feel intuitive to people who don't think in software

Benefits

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains.

Pay

The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary $320,000—$405,000 USD

Schedule

Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa Sponsorship

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.

How We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us!

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.