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.
Anthropic's Verticals team builds AI products purpose-built for specific industries—financial services, life sciences, healthcare, and legal. Most of these teams are being built 0→1 right now: you'll be forming the team, defining the product, and shipping the first version in markets where no one has done this well yet. Where we're further along, products are already live with enterprise customers and growing fast.
We're hiring Engineering Managers to lead the teams building Claude for Financial Services, Life Sciences, Healthcare, and Legal. You'll lead a team shipping AI into professional workflows—owning execution, working directly with customers and go-to-market, and helping shape where the broader Verticals group goes next.
We're hiring for all four verticals through this posting. Team placement happens during the interview process based on your background, interests, and organizational need—if you have deep experience in one of these domains, let us know in your application.
About the teams- Claude for Financial Services — Builds products for customers in investment banking, asset management, insurance, and corporate finance. Near-term work centers on deeply integrated experiences inside the tools these teams already use, with a roadmap expanding as we learn what's most useful. The team operates close to enterprise customers and close to research.
- Claude for Life Sciences — We're building an agentic research platform for scientists—orchestrating specialist agents for computational biology, literature review, and regulatory review—on top of model capabilities we're investing in for biology and chemistry. The product is live with early customers and expanding fast; you'll lead engineering through that growth
- Claude for Healthcare — We're earlier here: standing up a team to build 0→1, focused initially on payer workflows (claims, prior authorization, utilization management, member communications), with groundwork for clinical applications over time. You'll be defining the product and the team at the same time.
- Claude for Legal — Builds products for in-house legal teams and law firms—contract review and drafting, legal research, due diligence, and the document-heavy work that fills a lawyer's day. This team is forming now; you'll be one of the first leaders shaping what we build and who we build it with.
- Lead and develop a team of engineers building new AI products for enterprise customers in your vertical
- Work closely with research to make the models better in your domain—shaping evals, surfacing failure modes, and feeding customer learnings back into model development
- Own engineering execution end-to-end: planning, prioritization, delivery quality, team health, and incident response
- Partner with sales and customer success on enterprise deals—understanding requirements, joining key conversations, and turning what you learn into engineering priorities
- Shape the roadmap with product and design, not just execute against it
- Drive the compliance and platform-readiness work your customers require, partnering with security and legal
- Recruit, onboard, and grow strong engineers; give direct feedback and build a healthy, high-performing team
- Have built AI products and have a practical understanding of what it takes to turn model capabilities into applications people actually use
- Are comfortable working with enterprise customers, working alongside sales and customer success and joining customer conversations
- Know the operational realities of building on platforms and integrations you don't control
- Are a skilled engineering manager who treats management as a craft—clear feedback, strong 1:1s, consistent investment in your team's growth
- Experience in working with research to improve domain specific model capabilities
- Experience with model evaluation frameworks and how evals inform product decisions
- Experience taking a product from 0→1—forming a team, finding product-market fit, and shipping the first version with limited precedent to lean on
- Deep domain knowledge in one of these verticals—investment banking, asset management, insurance, or corporate finance; drug discovery or computational biology; clinical operations, health systems, or payers; or legal practice or legal tech
- Direct experience with the compliance frameworks relevant to these industries, including owning that work within an engineering org
- Exposure to both product-led growth and direct enterprise sales, and an understanding of how engineering decisions interact with each
- Partnering with an enterprise customer to map a core workflow—a deal-documentation process, a target-identification pipeline, a prior-authorization queue—and turning it into an engineering roadmap with a new or existing AI product
- Collaborating with research to design an evaluation framework that gives reliable signal on output quality across your domain's use cases
- Owning a platform-readiness initiative end-to-end—scoping with legal and security, defining the engineering work, and shipping it
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.
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.
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!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process
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What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
