Staff Software Engineer, Labs: Applied AI

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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.

• 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

You may be a good fit if you

• Have 8+ years of experience building full-stack applications, with a track record of zero-to-one work in startup or startup-like environments

• Are 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

• Are 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

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

• 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

Candidates need not have

• 100% of the skills listed above

• Formal certifications or education credentials

• Direct machine learning or AI research experience

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.

$320,000—$405,000 USD

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-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. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.