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About This Role
About Anthropic
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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.
About the role
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Anthropic is building the world’s most capable and safest AI — and our Finance team has a front\-row seat to deploy it. We are seeking a visionary Head of AI \& Innovation to lead the transformation of Anthropic’s Finance function through pioneering Claude AI deployment. In this role, you will set the strategic vision, build and coach a high\-performing team, and partner with Finance, Engineering, and Anthropic’s own product and research teams to create a Claude\-powered Finance ecosystem that serves as an industry\-defining model for AI\-enabled operations.
This is a rare opportunity to build at the intersection of cutting\-edge AI and enterprise finance — not just adopting AI, but pioneering what Finance AI looks like at scale with unprecedented access to the teams that build the models. You will own the Finance AI program end\-to\-end: from roadmap to production deployment, from governance frameworks to community building, and from employee enablement to enterprise scale. The ideal candidate brings deep Finance and Big 4 advisory experience and is a recognized thought leader in the industry — someone who shapes how the profession thinks about AI adoption, not just someone who follows it.
Responsibilities:
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Vision \& Strategic Leadership
- Define and own the multi\-year vision for a Claude\-enabled Finance ecosystem, translating Anthropic’s AI capabilities into measurable operational impact across all finance and accounting business processes and systems
- Establish Finance Systems as the internal gold standard for AI\-powered enterprise operations, positioning the team as a model for AI adoption across the broader organization
- Partner closely with Anthropic’s internal product and research teams to surface Finance use cases, influence model capabilities, and bring cutting\-edge AI research into production finance workflows
- Represent Finance AI priorities in cross\-functional forums and with executive leadership, communicating progress, risks, and strategic direction with clarity and conviction
- Serve as Anthropic’s external voice on Finance AI — engaging with Big 4 advisory networks, industry working groups, and peer communities to shape how the profession approaches AI\-enabled Finance transformation
Team Leadership \& Development
- Hire, mentor, and develop a small but high\-impact team of Finance AI Product Managers and Engineers, building a culture of experimentation, ownership, and continuous learning
- Establish clear roles, career paths, and operating rhythms for the AI \& Innovation pod, ensuring team members grow into industry\-leading Finance AI practitioners
- Partner with the Finance Systems PMO Lead and Head of SOX/IT Controls to ensure AI initiatives are sequenced, governed, and delivered in alignment with the broader transformation portfolio
Product Development \& Roadmap
- Own the Finance AI product roadmap — prioritizing use cases, managing the backlog, and driving features from ideation through production deployment
- Lead intake, triage, and routing of bug reports, defect tracking, and enhancement requests across the Finance AI platform, maintaining clear visibility into product health and velocity
- Oversee UX and design quality of Claude\-powered Finance tools, ensuring solutions are intuitive, delightful, and trusted by Finance end\-users
- Build and own a Finance AI Agent Registry — a centralized portfolio strategy for all deployed agents across Finance and Enterprise systems, defining how agents are discovered, evaluated, scaled, and retired as the agent landscape matures
- Lead evaluation and selection of Finance\-specific AI SaaS tools, partnering with BizTech and Data Infrastructure on architecture and integration decisions
Data Foundation \& Infrastructure
- Partner closely with Data Infrastructure and the Finance Analytics \& BI team to define the structured data layer powering AI\-driven reporting, anomaly detection, and decision support — ensuring Finance AI is built on a reliable, well\-governed data foundation
- Ensure AI agents are built on reliable, audit\-ready data foundations (SQL/deterministic retrieval layer) before applying AI synthesis and interpretation on top
- Collaborate with Finance Systems Engineers to design and maintain data pipelines, BigQuery environments, and integration architectures that support safe AI experimentation and production deployment
Enablement, Governance \& Community
- Design and lead Finance AI onboarding and employee journey programs — building the training curriculum, certification pathways, and ongoing education cadence that drives adoption across the Finance organization
- Partner with the Internal Audit and Governance team to develop Finance\-specific AI usage policies, responsible AI frameworks, and audit\-ready documentation for deployed agents
- Enforce SOX\-compliant AI governance frameworks in close partnership with the Head of SOX/IT Controls, ensuring AI deployments meet ITGC requirements and external audit standards
- Organize internal innovation events, creating space for experimentation, cross\-functional collaboration, and identification of the next generation of AI use cases
- Build a Finance AI peer community — hosting internal and external events, publishing learnings, and deepening Anthropic’s relationships with Big 4 advisory networks and Finance AI industry forums to advance the field and strengthen Anthropic’s brand as an AI\-first organization
You may be a good fit if you:
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- Have 15\+ years of experience spanning Finance operations, enterprise technology, and product or program leadership, with a demonstrated track record of leading large\-scale transformation initiatives
- Have led AI, automation, or digital transformation programs in a Finance or Enterprise context, with hands\-on experience taking AI/LLM solutions from prototype to production
- Possess deep Finance domain knowledge across multiple functions (OTC, P2P, FP\&A, accounting, close) and understand how AI can materially change how Finance work gets done
- Bring established relationships and active engagement within Big 4 advisory communities (Deloitte, PwC, EY, KPMG) and are recognized as a thought leader in Finance AI or enterprise Finance transformation
- Have built and developed high\-performing technical and product teams, with a coaching mindset and the ability to grow early\-career practitioners into domain experts
- Are a skilled cross\-functional leader with a proven ability to influence and align stakeholders across Finance, Engineering, Data, IT, and executive leadership
- Bring strong product instincts — you think about user experience, adoption, and measurable outcomes, not just technical capability
- Have experience working in high\-growth or scaling technology companies, and understand the compliance, governance, and control requirements of a maturing enterprise Finance function
- Are deeply curious about AI’s potential, excited to work directly alongside the teams building frontier models, and motivated by the opportunity to define what Finance AI looks like at Anthropic and for the industry
Strong candidates may also have:
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- A background in Big 4 advisory (Deloitte, PwC, EY, KPMG) in a Finance transformation, Finance systems, or AI/digital practice — with an active professional network and a track record of publishing, speaking, or advising in the space
- Hands\-on experience building with LLM APIs (including Claude) or deploying AI agents in enterprise Finance workflows
- Familiarity with SOX 404(b) compliance, ITGC controls, and the intersection of AI governance with financial audit requirements
- Experience with Finance data platforms (BigQuery, Snowflake), ETL pipelines, and the data architecture patterns that underpin reliable AI\-driven Finance reporting
- Background in ERP ecosystems and the systems integration challenges of deploying AI across multi\-system Finance stacks
- Experience with change management frameworks and organizational enablement programs at scale — particularly driving adoption of novel tooling in Finance functions
- Conference speaking, published writing, or active participation in Finance AI industry working groups — with a presence that elevates Anthropic’s reputation in the field
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:
$315,000 \- $365,000 USDLogistics
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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
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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!
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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
Salary Context
This $315K-$365K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Anthropic, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($340K) sits 104% above the category median. Disclosed range: $315K to $365K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Anthropic AI Hiring
Anthropic has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $320K - $450K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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