Job description

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. About the role Anthropic's customer-facing teams are often the first place a customer sees what Claude can do. As a Demo Engineer, you'll build the demos, reference implementations, and reusable tooling that help those teams make new capabilities tangible, relevant, and trustworthy, ideally within days of a product launch rather than weeks after. This role sits between product engineering, technical storytelling, and GTM execution. You'll own ambiguous, high-leverage problems end to end: prototyping an agentic experience for a new capability, then hardening it into something reliable that other teams can run on their own. You'll work closely with Product, Engineering, Marketing, Operations, and our sales and solutions teams, and you'll bring what you learn from customer-facing work back to the people building the products. Because a demo is frequently someone's first impression of what our models can and can't do, accuracy and honesty matter as much as polish. Success in this role looks like customer-facing teams demonstrating new capabilities sooner and with more confidence, recurring requests becoming reusable assets rather than one-off projects, and field insights shaping product and launch-readiness decisions. Responsibilities Own demo readiness for new and priority Claude capabilities, including environments, integrations, synthetic data, evaluations, reliability checks, and fallback paths Build prototypes, agents, and reference flows that make complex capabilities concrete and tie them to clear customer outcomes Turn recurring requests into reusable accelerators such as companion apps, templates, Skills, plugins, and self-service demo packages that scale across customer-facing teams Partner with GTM colleagues on high-impact customer moments, unblock critical technical gaps, and spot the patterns worth turning into shared assets Build internal agentic systems that automate demo preparation, validation, and asset maintenance, so demos stay current as products change Translate patterns from customer-facing work into product feedback, launch-readiness priorities, and systemic improvements with Product and Engineering Define and raise the bar for demo quality across accuracy, safety, reliability, usability, and adoption Decide when a customer need calls for a bespoke build and when it should become a shared capability You may be a good fit if you have Proficiency in Python or TypeScript, with experience shipping working software end to end Hands-on experience building applications or agents on top of LLM APIs, including prompting, tool use, and retrieval Experience designing integrations with third-party APIs, data sources, and services The ability to take an ambiguous capability or customer need to a working prototype quickly, and then make it reliable Clear communication of technical concepts to both technical and non-technical audiences, including sales and solutions teams Comfort collaborating across Product, Engineering, Marketing, Operations, and GTM teams Experience in a demo engineering, solutions engineering, sales engineering, forward deployed engineering, developer relations, or product engineering role A track record of building reusable internal tools, templates, or self-service assets that other teams adopted Hands-on experience with Claude Code, the Claude Agent SDK, the Model Context Protocol (MCP), Skills, or plugins Experience building evaluation or validation harnesses for AI systems, including reliability checks and graceful fallbacks Experience building always-on or scheduled agentic systems that automate internal workflows Experience building synthetic or sanitized datasets that make demos realistic without exposing sensitive information Front-end or product design skills that help demos feel polished and intuitive Experience supporting product launches or running launch-readiness programs A history of turning field feedback into concrete product input that influenced a roadmap A passion for making powerful technology safe and beneficial, and a commitment to representing model capabilities accurately and honestly 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: $240,000 — $315,000 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.

Requirements

Must-haves

Own demo readiness for new and priority Claude capabilities, including environments, integrations, synthetic data, evaluations, reliability checks, and fallback paths
Build prototypes, agents, and reference flows that make complex capabilities concrete and tie them to clear customer outcomes
Turn recurring requests into reusable accelerators such as companion apps, templates, Skills, plugins, and self-service demo packages that scale across customer-facing teams
Partner with GTM colleagues on high-impact customer moments, unblock critical technical gaps, and spot the patterns worth turning into shared assets
Build internal agentic systems that automate demo preparation, validation, and asset maintenance, so demos stay current as products change
Translate patterns from customer-facing work into product feedback, launch-readiness priorities, and systemic improvements with Product and Engineering
Define and raise the bar for demo quality across accuracy, safety, reliability, usability, and adoption
Decide when a customer need calls for a bespoke build and when it should become a shared capability
You may be a good fit if you have
Proficiency in Python or TypeScript, with experience shipping working software end to end
Hands-on experience building applications or agents on top of LLM APIs, including prompting, tool use, and retrieval
Experience designing integrations with third-party APIs, data sources, and services
The ability to take an ambiguous capability or customer need to a working prototype quickly, and then make it reliable
Clear communication of technical concepts to both technical and non-technical audiences, including sales and solutions teams
Comfort collaborating across Product, Engineering, Marketing, Operations, and GTM teams
Experience in a demo engineering, solutions engineering, sales engineering, forward deployed engineering, developer relations, or product engineering role
A track record of building reusable internal tools, templates, or self-service assets that other teams adopted
Hands-on experience with Claude Code, the Claude Agent SDK, the Model Context Protocol (MCP), Skills, or plugins
Experience building evaluation or validation harnesses for AI systems, including reliability checks and graceful fallbacks
Experience building always-on or scheduled agentic systems that automate internal workflows
Experience building synthetic or sanitized datasets that make demos realistic without exposing sensitive information
Front-end or product design skills that help demos feel polished and intuitive
Experience supporting product launches or running launch-readiness programs
A history of turning field feedback into concrete product input that influenced a roadmap

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythontypescriptllm

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

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