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About This Role
About Anthropic
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.
As a manager of Applied AI Engineers for Anthropic's Megas segment, you will lead a team of deeply technical engineers embedded with our largest and most strategic accounts. These companies are partner-customers: they are among our biggest revenue drivers and also some of our most important go-to-market distribution channels. Each one consumes Claude to transform its own business and distributes Claude through its platforms and marketplaces. Your team works hands-on with their product and engineering organizations as they build on Claude: co-designing complex technical solutions, advising on architecture, developing evaluation frameworks, and guiding them through the most advanced implementation patterns for LLMs.
You will hire, coach, and grow this team while staying close enough to the technology to set a high bar for engineering judgment. Your engineers work in account-aligned pods alongside Applied AI Architects, and you'll partner closely with pod leads, Mega Sales, Partnerships, Product, and Research. You'll guide your team from technical discovery through production deployment, turn wins at one Mega into reference architectures and patterns that compound across the others, and channel Mega-driven requirements back into Anthropic's product and research roadmap, all while maintaining our high standards for safety and reliability. This is a fast-moving space at the frontier of what's possible with LLMs, and you'll help define how we scale technical impact across our most important relationships.
Responsibilities
- Build, coach, and grow a team of Applied AI Engineers serving Mega accounts: hire strong technical builders, develop them through structured feedback and clear competency expectations, and create scalable onboarding for a customer-facing technical role.
- Staff engineers into account-aligned pods alongside Applied AI Architects, balancing deep account continuity with flexibility to move expertise where the segment needs it most.
- Set performance goals aligned with each Mega's technical account plan and the segment's consumption objectives, and establish the metrics, reporting cadence, and dashboards that keep pod leads and cross-functional stakeholders informed on team health and impact.
- Serve as a senior technical advisor to Mega product and engineering leaders as they deploy production Claude workloads: guiding architecture design, evaluation strategy, and advanced prompting, agentic, and implementation patterns from discovery through deployment.
- Lead hands-on co-build initiatives with Mega engineering teams, from prototypes and pilots to production deployments, in support of each account's joint roadmap with Anthropic.
- Help navigate the partner-customer duality: support both direct consumption and the distribution of Claude through Mega platforms and marketplaces, and exercise sound judgment about what we build together and share.
- Act as a technical escalation point for your team's accounts, and partner with pod leads and Mega Sales to sequence technical work for maximum customer value.
- Build shared assets across pods, including reference architectures, agentic patterns, and eval frameworks, so that what one pod learns benefits every Mega account.
- Collaborate with Product and Research to surface Mega-driven requirements with the right weight and context, advocating for high-impact product changes without becoming a feature-request pass-through.
- Champion scalable public and internal assets documenting the latest LLM prompting, evaluation, agentic, and architecture techniques; contribute thought leadership through talks, blog posts, and white papers.
- Maintain strong, current knowledge of LLM capabilities, implementation patterns, and the AI product development stack, and translate it into practical guidance for your team and customers.
- Travel to customer sites for workshops, executive technical sessions, implementation support, and relationship building, and represent Anthropic at conferences and speaking engagements.
You may be a good fit if you have
- 6+ years of experience in technical roles such as Forward Deployed Engineer, Software Engineer, Solutions/Sales Engineer, or Technical Product Manager.
- 4+ years of experience operating in a formal leadership capacity, ideally with some tenure as a manager of managers/multiple teams. Former technical founders are also encouraged to apply.
- Demonstrated success hiring, coaching, and scaling customer-facing technical teams, ideally in a fast-moving or hypergrowth environment.
- Experience supporting large, named strategic accounts at a platform, infrastructure, or model company, ideally where the account was both a customer and a partner.
- Production experience with LLMs, including advanced prompt engineering, agent development and frameworks, evaluation frameworks, transcript analysis, MCP, and deployment at scale, with the technical depth to set engineering standards and review your team's work credibly.
- Strong programming skills with proficiency in Python or TypeScript and experience building production applications.
- A track record engaging senior technical stakeholders (CTOs, CPOs, and engineering leaders) and influencing technical architecture and partner product roadmaps.
- Experience working in or running mixed pre-sales and hands-on engineering teams (for example, SA + FDE or SE + delivery).
- The ability to navigate ambiguity and execute across domains with intellectual openness, finding simple solutions to complex problems.
- A high-cooperation mindset for cross-organizational collaboration across Sales, Partnerships, Product, and Research, balancing competing priorities with integrity.
- Exceptional communication skills to convey technical concepts to diverse stakeholders while maintaining a low ego and collaborative approach.
- Passion for advancing safe, beneficial AI systems through creative technical applications.
Strong candidates may also have
- Background at a hyperscaler, foundation model lab, or large ISV in a strategic-accounts or alliances technical role.
- Experience with marketplace, embed/OEM, or co-sell motions.
- Prior forward-deployed engineering leadership.
- Experience leading technical teams through periods of rapid scale, balancing immediate execution with durable, long-term architectural and platform decisions.
- Experience coaching engineers from purely technical roles into customer-facing, advisory work.
- Executive briefing and public speaking presence.
Deadline to apply
None. Applications will be reviewed on a rolling basis.
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:
$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.
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.
Salary Context
This $320K-$405K range is above the 75th percentile for AI Engineering Manager roles in our dataset (median: $211K across 6 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Anthropic, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $251,622 based on 51 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($362K) sits 44% above the category median. Disclosed range: $320K to $405K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Anthropic AI Hiring
Anthropic has 52 open AI roles right now. They're hiring across AI Software Engineer, AI Engineering Manager, AI/ML Engineer, Research Engineer. Positions span New York, NY, US, San Francisco, CA, US, Washington, DC, US. Compensation range: $315K - $850K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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