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Anthropic

Applied AI Architect, Commercial

San Francisco, CA | New York City, NY; New York City, NY; San Francisco, CAOnsite · United States
Staff · Applied AI
POSTED 17 APRVERIFIED 9M AGO
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Pay · posted
$240k–315k
H-1B median $156k
Open for
199d
since 17 Apr
H-1B · FY26
191
UK sponsor
Hiring · 30d
672
−5 net · open roles
PythonLLMs

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

As an Applied AI team member at Anthropic, you will be a Pre-Sales architect focused on becoming a trusted technical advisor helping customers understand the value of Claude and paint the vision on how they can successfully integrate and deploy Claude into their technology stack. You'll combine your technical depth with customer-facing skills to architect innovative LLM solutions that address complex business challenges while maintaining our high standards for safety and reliability.

As a Commercial Solutions Architect, you'll go deep with priority accounts as a hands-on builder, while creating reusable blueprints, demos, and enablement that extend Claude's reach across the broader Commercial book of business.

Working closely with our Sales, Product, and Engineering teams, you'll guide customers from initial technical discovery through successful deployment. You'll leverage your expertise to help customers understand Claude's capabilities, develop evals, and design scalable architectures that maximize the value of our AI systems.

Responsibilities

Partner with account executives to deeply understand customer requirements and translate them into technical solutions, ensuring alignment between business objectives and technical implementation

Serve as the primary technical advisor to customers throughout their Claude adoption journey, from discovery to initial evaluation through deployment. You will need to coordinate internally across multiple teams and stakeholders to drive customer success

Support customers building with the Claude API, Claude Code, and Claude for Enterprise

Ship working code. Build prototypes and proof-of-concepts hands-on, develop eval frameworks, and write near-production examples that customers can extend

Build reusable blueprints, demos, and enablement assets that scale across customers

Guide technical architecture decisions and help customers integrate Claude effectively into their existing technology stack

Help customers develop evaluation frameworks to measure Claude's performance for their specific use cases

Identify common integration patterns and contribute insights back to our Product and Engineering teams

Travel occasionally to customer sites for workshops, technical deep dives, and relationship building

Maintain strong knowledge of the latest developments in LLM capabilities and implementation patterns

You may be a good fit if you have

3+ years of highly technical experience as a software engineer (or equivalent) with some customer-facing exposure, OR 3+ years as a Solutions Architect, Sales Engineer, or Technical Account Manager with strong hands-on building experience

A builder identity. You've shipped real software, you have technical taste, and you care about the craft of what you build

A systems mindset. When you see a problem, your instinct is to ask "how do I make this reusable." You'd rather build one thing that serves ten customers than ten things that serve one each

Strong coding ability. You ship prototypes regularly and can work in a real codebase, not just notebooks. Comfort with Python expected

Strong ability to build trust with technical stakeholders and adjust your communication for varied audiences

Strong technical communication skills with the ability to translate customer requirements between technical and business stakeholders

Experience designing scalable cloud architectures and integrating with enterprise systems

Familiarity with common LLM frameworks and tools, or a background in machine learning or data science

Comfort operating in early-stage, ambiguous environments where the playbook doesn't exist yet, and a track record of building structure as you go

Source · Anthropic · GreenhouseFirst seen 3 NovOriginal ↗Method