Available Locations: Austin, TX | New York City, NY | San Francisco, CA

About AI Gateway

AI inference is becoming core infrastructure. Every serious application will need access to many models across many providers, with reliability, observability, security, cost control, and routing built in from the start. AI Gateway is Cloudflare's bet that this layer belongs at the network edge: close to users, close to compute, and simple enough that a developer can adopt it with one API change.

Developers using AI Gateway need answers to two questions. Is the model I'm calling working the way you say it does? And which model should I be using at all? You'll lead the models team that answers both.

You'll start with model operations. That's the work behind every model in the catalog: provider integrations, capability testing, pricing and metering, capacity, security reviews, docs, health, and deprecations. Sometimes a launch needs to go live within minutes of a lab's announcement. Sometimes the right call is to hold it. Cloudflare is an infrastructure company at heart, so reliability and accuracy come first; speed comes from preparation and automation.

Then you'll bring model intelligence online. That means turning what we learn from Gateway traffic into decisions for customers: capability maps, per-task rankings, auto-routing profiles that pick the right model for each request, evals and regression detection, and market intelligence developers can trust.

This is a people-management role reporting to the AI Gateway Engineering Manager. You'll hire, develop, and lead the team, and you'll stay hands-on: writing code, building agent-driven workflows, and getting into the details of a failed launch or a suspicious eval SLO. Expect to manage people, projects, model stock, and operations all at once.

About the Role

• Build and lead the models team: Hire, coach, and grow the people who run the catalog today and build its intelligence over time. Give people real ownership, give direct feedback, and grow leads who can run their own areas.
• Run model launches end-to-end: Scope integrations, coordinate testing and evals, confirm pricing, metering, capacity, and security, and get docs and announcements ready. Prepare day-zero launches in advance so they can ship in minutes.
• Keep the catalog true: Make sure what we publish (capabilities, limits, versions, prices) matches what developers actually get. Catch silent regressions, such as broken tool calling, truncated streams, quantization changes, or usage and pricing mismatches, before customers do.
• Build model intelligence: As it comes online, own the evals, capability maps, and per-task rankings behind our auto-routing profiles and model recommendations. Make sure they measure what they claim to, and connect regressions to routing decisions so customers who chose "auto" aren't the ones who discover a problem.
• Treat the catalog and intelligence as products: Have a point of view on which models belong in the catalog, which routing profiles to ship, and what market intelligence to publish. Base it on usage, quality, reliability, and cost data. Turn what you learn into roadmap input.
• Run a lightweight operating cadence: Keep one source of truth for what's in flight, who owns it, what's blocked, and what ships next. Keep Engineering, Product, Partnerships, and other stakeholders aligned and informed without status meetings.
• Automate relentlessly: Turn repeatable steps into scripts, tools, and agent workflows: endpoint validation, eval runs, catalog and pricing reconciliation, regression detection, and provider follow-ups. Manual effort per launch should fall as the catalog grows.

Responsibilities

• Day-Zero & Quality Launches: Ensure new models launch on day zero with tested capabilities, accurate pricing, docs, and a clear call on routing eligibility.
• Catalog Reliability & Health: Ensure the catalog reflects reality, and proactively identify when a model or provider is underperforming before customers notice.
• Auto-Routing & Optimization: Deliver auto-routing that measurably beats a fixed model choice on cost or quality for users.
• Developer Trust & Methodology: Build and maintain trusted rankings and recommendations grounded in solid methodology.
• Partner & Stakeholder Management: Set clear expectations for providers, while keeping Engineering, Product, Partnerships, and other stakeholders aligned.
• Team Leadership & Growth: Provide clear ownership and direction so the team knows what's next, who owns it, and how to grow into bigger roles.
• Operational Scalability: Continually drive down the human effort required for each launch, eval, and routing update through tools and automation.

Desirable Skills, Knowledge, and Experience

• People Leadership: Experience hiring, managing, and growing engineers or operators, giving direct feedback, delegating real ownership, and expanding scope without becoming a bottleneck.
• Technical & Shipping Capabilities: Proven track record of taking technical launches or integrations from ambiguity to production across teams and partners. Hands-on ability to write TypeScript, Python, or Rust, read API docs, inspect endpoints, and debug using terminal tools (logs, cURL).
• Model Ecosystem Expertise: Deep familiarity with new models, labs, and inference providers. Hands-on understanding of streaming, tool calling, structured outputs, rate limits, token pricing, and provider-specific model behavior variations.
• Product & SRE Mindset: Data-driven product instincts to advocate for adding, modifying, or retiring catalog items. SRE approach to defining, measuring, and alerting on service health, managing incidents calmly, and fixing root causes.
• Agent Workflows & Detail-Oriented Execution: Daily experience building with agents—scoping work, verifying outputs, and integrating deterministic checks or human judgment appropriately. Meticulous attention to detail regarding model IDs, context limits, per-token prices, and response fields.

Bonus Qualifications (Nice to Have):

• Experience at an inference provider, model lab, model router, eval platform, or API company.
• Background in evals, ranking/recommendation systems, observability, metering, or usage-based billing.
• Familiarity with Cloudflare Workers, AI Gateway, Workers AI, or Replicate.
• Relevant domain backgrounds in engineering management, SRE, partner engineering, evals/applied ML, technical program management, or solutions engineering.

Compensation

Compensation may be adjusted depending on work location.

• For Bay Area based hires: Estimated annual salary of $230,000 - $317,000
• For New York City, New Jersey, Washington, Washington DC, and California (excluding Bay Area) based hires: Estimated annual salary of $275,000 - $303,000

Equity

This role is eligible to participate in Cloudflare’s equity plan.

Benefits

Cloudflare offers a complete package of benefits and programs to support you and your family. Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun! The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S.

Health & Welfare Benefits

• Medical/Rx Insurance
• Dental Insurance
• Vision Insurance
• Flexible Spending Accounts
• Commuter Spending Accounts
• Fertility & Family Forming Benefits
• On-demand mental health support and Employee Assistance Program
• Global Travel Medical Insurance

Financial Benefits

• Short and Long Term Disability Insurance
• Life & Accident Insurance
• 401(k) Retirement Savings Plan
• Employee Stock Participation Plan

Time Off

• Flexible paid time off covering vacation and sick leave
• Leave programs, including parental, pregnancy health, medical, and bereavement leave