Manager, Applied AI Engineering (Megas)

• 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 goo

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, Partne