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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.

Responsibilities

• 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

You may be a good fit if you

• 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

Strong candidates may also have

• 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

Representative projects

• 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

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.

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Financial services is one of the most consequential domains for AI—and one of the most demanding. Workflows are complex, data is sensitive, regulatory expectations are real, and the people using these tools are experts who will immediately know if something doesn't work. That's exactly the kind of challenge we're here for.

Anthropic's Verticals team builds AI-powered products purpose-built for the industries where this complexity is highest. We're in early growth, moving fast, and earning the trust of enterprise customers who have seen a lot of vendors promise transformation and deliver demos. This role is about building something that actually changes how financial services teams work—and building the team that can do that sustainably.

Responsibilities

• Lead and develop a team of engineers building AI-powered experiences in Excel and PowerPoint for financial services enterprise customers

• Own engineering execution end-to-end: project planning, prioritization, delivery quality, team health, and incident response

• Partner closely with sales and customer success teams on enterprise deals—understanding customer requirements, participating in key conversations, and translating what you learn into engineering priorities

• Work with product and design to shape the roadmap, not just execute against it; you'll have a meaningful voice in what we build and why

• Maintain operational reliability on top of third-party platforms (the Microsoft 365 ecosystem), and build the processes that make your team resilient when those dependencies behave unexpectedly

• Engage with research and evaluation frameworks—developing intuition for model behavior, understanding evals, and helping the team make sound tradeoffs between capability and reliability

• Drive compliance and regulatory initiatives relevant to your customers, including owning the internal engineering work required to meet them

• Recruit, onboard, and develop strong engineers; give direct feedback, grow careers, and build a team culture that earns the confidence of enterprise customers

You may be a good fit if you

• Are a skilled engineering manager who takes the craft of management seriously: clear feedback, strong 1:1s, hard conversations handled well, and consistent investment in your team's growth

• Have experience building products for or within financial services—you understand how these organizations work, what they care about, and why trust and reliability aren't negotiable

• Know how to operate in an enterprise sales environment; you're comfortable alongside sales and customer success teams and can hold your own in customer conversations

• Have shipped AI-powered products and developed a grounded, practical understanding of what it takes to make them reliable and useful in high-stakes contexts

• Are experienced with the operational realities of building on third-party platforms—you've thought through degradation strategies, incident response, and the accountability gaps that come with dependencies you don't control

• Thrive in early-growth environments where the product is real but the playbook is still being written

Strong candidates may also have

• Deep domain knowledge in financial services—investment banking, asset management, insurance, corporate finance, or similar—whether from working within these institutions or building products for them

• Direct experience with compliance frameworks relevant to financial services and healthcare, and a track record of owning or driving compliance initiatives within an engineering organization; familiarity with HIPAA is a meaningful differentiator

• Experience managing teams that use AI-assisted coding tools, and a considered perspective on what that means for code review, quality standards, and engineering norms

• Exposure to both product-led growth and direct enterprise sales motions, with an understanding of how engineering decisions interact differently with each

• Vendor management experience—negotiating with, evaluating, or operationalizing third-party technology providers

• Familiarity with model evaluation frameworks and how evals can inform product decisions, not just research ones

Representative projects

• Partnering with an investment banking customer to understand their deal documentation workflow, then working with product to translate that into a concrete engineering roadmap

• Building an incident response and communication playbook for outages or degradation in Microsoft 365 integrations—and running the post-mortems that drive real improvements

• Owning a compliance initiative from scoping through delivery: working with legal and security, defining what engineering needs to build, and getting your team across the line

• Collaborating with a cross-functional team to develop a new AI-powered product feature, and working with stakeholders to ensure its successful launch

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Microsoft are looking for a talented Machine Learning Software Engineer at their New York office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising artificial intelligence technology. You'll work directly with leadership to shape the company's direction in the technology and simulation markets.

About the Role

As a Member of Technical Staff – Software Engineer & Machine Learning, you will work building AI Insights, a Copilot analytics product that enables our internal stakeholders to move from “What happened?” to “Why did it happen?” in minutes. You’ll design and implement AI-driven trend detection, cohort analysis, and drill-down workflows that connect metrics to real user conversations.

Accountabilities

• Build scalable data pipelines for telemetry ingestion, anomaly detection, and cohort segmentation.
• Implement ML-driven insights (prompted classifiers, anomaly detection) and integrate them into dashboards and APIs.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Technical skills:

• Proven experience leading small engineering and machine learning teams, and collaborating effectively with cross-functional stakeholders including product managers, UX designers, and security specialists.
• Demonstrated interest in Responsible AI.

Personal attributes:

• Hands-on with observability (metrics, tracing, logs) and model evaluation frameworks.

Benefits

• Competitive salary range of USD $139,900 – $274,800 per year.
• Benefits and other compensation.
• Opportunities for professional development and growth within the company.

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