Head of Support Engineering

Posted Yesterday
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San Francisco, CA, USA
In-Office
220K-335K Annually
Senior level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
Build and lead Exa’s global, AI-first 24/7 support organization. Hire and manage Support Engineers, establish tooling, SLAs, severity models, automations, and escalation processes, and oversee customer and engineering escalations. Use AI to resolve tickets efficiently, monitor account health and churn risks, partner with account teams, convert support trends into product improvements, and report on support performance. The role requires hands-on programming, API debugging, customer integration expertise, and technical leadership experience.
Summary Generated by Built In
About Exa

We raised a $250M Series C to build the search engine for AIs. Led by a16z, with existing investors Benchmark, Lightspeed, and YC doubling down, the round brings Exa's valuation to $2.2 billion.

Exa is building a search engine for the AI era. Our Search API currently powers Agents, Fortune 500s, and AI labs as we transform an industry that hasn't been disrupted since the 90s. We're a largely SF-based team of ~100 from Harvard, MIT, Meta, Google Research, ex-founders, and dropouts alike. We are rapidly building the most intelligent search engine in history. We're high agency, low-ego, and united by the feeling that this is one of the last problems worth getting right.

About the Role

You'll build and lead Support at Exa: an AI-first, 24/7 support function for every committed customer, worldwide. AI tools like Pylon and AI agents handle the first line; your team of Support Engineers takes on the hard technical problems the AI can't, with the same technical depth as our Forward Deployed Engineers.

You'll also own escalation management: watching the health of every account, largely through AI, and making sure every contraction or churn risk reaches the right people with a clear plan to fix it.

This is a technical leadership role. You hold the same technical bar as your team: you can debug a customer integration, read the code, and open a pull request yourself.

In this role, you will:
  • Build the Support team: hire, onboard and lead Support Engineers across regions for 24/7, follow-the-sun coverage

  • Build the support system: own our Pylon setup, ticket and SLA tracking, severity levels, automations and the end-of-shift handoffs, so the function runs as efficiently as possible

  • Make the AI first line work: decide which tickets the AI resolves and which reach a human, and keep improving the share it resolves without a person

  • Own escalations to Engineering: a clear path with full context, and customer follow-up owned by Support until resolution

  • Own escalation management: monitor account health across regions, flag every contraction or churn risk to the account team and leadership with requested actions and owners, and follow it until resolved. Escalation management is a duty of your Support Engineers; add dedicated people only if the load requires it

  • Partner with the account team (AEs, account managers, FDEs) on at-risk and strategic accounts

  • Turn ticket patterns into product feedback, and drive fixes with Product and Engineering

  • Report on support health: response and resolution times, customer satisfaction, AI resolution rate, and churn and contraction avoided

Requirements
  • Several years of hands-on programming experience, at the same level as the Support Engineers you'll hire: you can debug APIs and customer integrations and open pull requests yourself

  • 5+ years in technical support, support engineering, SRE or customer engineering, including 2+ years leading a technical support team, ideally at an API, developer-tools or AI company

  • Experience building support operations from scratch: tooling, SLAs, severity models, escalation paths and 24/7 coverage

  • Strong judgment on automation and AI: you've used AI or automation to scale a support function without lowering quality

  • Clear communication with customers, engineers and executives, especially under pressure

You might excel if you...
  • Have built or rebuilt a support function at a fast-growing technical company

  • Have run Pylon, or a similar support platform, at scale

  • Have built with LLMs, RAG or agents

  • Care deeply about the problem of organizing the world's knowledge and making it accessible to machines

This is an in-person opportunity in San Francisco We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3). In addition to premium healthcare benefits (medical, dental, vision), we also offer fertility benefits and a monthly wellness stipend to all of our employees.

Exa is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, age, veteran status, marital status, pregnancy or related conditions, criminal histories consistent with applicable law, or any other basis protected by applicable law.

Skills Required

  • Several years of hands-on programming experience, including the ability to debug APIs and customer integrations and open pull requests
  • 5+ years in technical support, support engineering, SRE, or customer engineering
  • 2+ years leading a technical support team
  • Experience building support operations from scratch, including tooling, SLAs, severity models, escalation paths, and 24/7 coverage
  • Experience using AI or automation to scale a support function without lowering quality
  • Strong communication skills with customers, engineers, and executives, especially under pressure
  • In-person work in San Francisco
  • Experience building or rebuilding a support function at a fast-growing technical company
  • Experience running Pylon or a similar support platform at scale
  • Experience building with LLMs, RAG, or agents
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The Company
HQ: Burlington, MA
86 Employees
Year Founded: 2021

What We Do

Exa was built with a simple goal — to organize all knowledge. After several years of heads-down research, we developed novel representation learning techniques and crawling infrastructure so that LLMs can intelligently find relevant information.

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