Staff+ Software Engineer, Vertical AI Products (Multiple Roles)

Anthropic•San Francisco, CA

2d•$405,000 - $485,000•Hybrid

About The Position

Anthropic's Verticals team builds AI products purpose-built for specific industries — financial services, science, healthcare, and the broader enterprise. Most of these teams are being built 0→1 right now: you'll be shaping the product and the architecture 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 Staff+ Software Engineers to build the products that bring Claude into financial services, science, healthcare, and enterprise AI workflows. You'll be a technical leader who thinks holistically about the end-to-end customer experience, partners directly with research to push model capabilities into production, and carries real ownership over what we ship next. We're hiring across all of these areas 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. 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 Science — We just launched Claude Science, an AI workbench for scientists that brings fragmented research tools into a single environment. The product is live and expanding fast; you'll help drive engineering through that growth. Claude for Healthcare — The focus is payer workflows (claims, prior authorization, utilization management, member communications), with groundwork for clinical applications over time. You'll be shaping the product and the architecture at the same time. Enterprise AI Products — Building what makes Claude a daily-use tool for enterprise customers across industries: plugins, skills, and shared organizational context — the connective tissue that lets Claude operate across an organization's workflows — plus the foundational systems large organizations require to deploy AI at scale, like user and permissions management, security and compliance features, and analytics infrastructure. A big part of this work is understanding what's blocking adoption and building the capabilities that close those gaps.

Requirements

8+ years of software engineering experience, ideally with 2+ years at a Staff or equivalent technical leadership level
Have led the design and delivery of complex enterprise or B2B products across the full stack
Have built AI products and know what it takes to turn model capabilities into applications people actually use
Are comfortable working directly with enterprise customers and translating what you learn into technical decisions
Have built products from 0 to 1 in fast-moving environments, and can set technical direction with limited precedent to lean on
Drive cross-team alignment to ship impactful work, with influence over authority

Nice To Haves

Experience working with research to improve domain-specific model capabilities, including evaluation frameworks
Deep domain knowledge in one of these areas: investment banking, asset management, insurance, or corporate finance; scientific research or computational biology; clinical operations, health systems, or payers; or enterprise platform work
Exposure to both product-led growth and direct enterprise sales

Responsibilities

Own technical design and delivery for a core piece of one of these vertical or enterprise products, end-to-end across the stack
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
Partner with product, design, and go-to-market to turn enterprise customer workflows into shipped product, not just execute against a spec
Set technical direction and standards for your team — architecture, code quality, and how the team builds
Work directly with enterprise customers and sales during key conversations, translating what you learn into engineering priorities
Mentor other engineers and raise the technical bar across the team, working with influence rather than authority