• Software maintenance and production troubleshooting have become a massive tax of engineering velocity. Resolve AI is solving this by building a transformative, truly autonomous AI Production Engineer that investigates and fixes complex system issues end-to-end.
• Our founders ( Spiros Xanthos and Mayank Agarwal ) are the core creators of OpenTelemetry and led Splunk Observability. They have had 2 successful exits to Splunk and VMware.
• We’ve raised over $190M from top-tier investors including Lightspeed, Greylock, DST, Unusual Ventures, and individual backers such as Jeff Dean (Chief Scientist, Google DeepMind), Thomas Dohmke (CEO, GitHub), Matt Garman (CEO, AWS), Reid Hoffman (Founder, LinkedIn), and Fei-Fei Li (Professor, Stanford).

Our customers run ResolveAI against their most critical production systems, so when something goes wrong, our response has to be as reliable as the systems they trust us with. As our first Head of Technical Support, you’ll build that response from the ground up: a 24x7, global support function that is AI-first by design, where agents resolve as much as possible and humans step in where judgment, context, and empathy matter most.

This is a builder role. You’ll design the support model, set the service commitments, choose and build the tooling, work cases yourself, and hire the team as we scale. You’ll be the third pillar of Customer Engineering, alongside Solutions Engineering and Deployment Engineering, and you’ll make sure customers get a dependable answer without our SEs and DEs becoming the default support queue.

This role is for a hands-on technical support leader who has supported developer or infrastructure products, understands production incidents from the inside, and is excited to run support on the same agentic technology our customers are buying.

• Design and launch the support operating model. Define how we deliver 24x7 global coverage, including severity levels, response and resolution targets, escalation paths, on-call rotations, and follow-the-sun handoffs, and turn them into service commitments we can stand behind in customer contracts.
• Build an AI-first support experience. Use Resolve and other AI agents to triage, diagnose, and resolve cases, deflect common issues through self-service and documentation, and design the hand-off from agent to human so customers never lose context. Measure and steadily raise the share of cases resolved without human intervention.
• Work the queue yourself. Own cases end-to-end in the early days: triage, gather diagnostics, troubleshoot across integrations, cloud environments, and Kubernetes, keep the case record current, and communicate clearly with customers through resolution.
• Own the case, every time. Make sure every case has a clear owner, a documented impact and investigation, coordinated customer updates, and a documented resolution, including during high-severity incidents where the customer’s own production is on the line.
• Run a tight loop with Engineering. Separate configuration and usage issues from genuine product defects, raise well-evidenced Linear tickets with clear customer impact, and give the assigned Deployment Engineer the evidence they need to oversee the fix and verify it in the customer’s workflow.
• Partner with the account team. Bring in the technical account team when account history or implementation context is needed, share case trends and customer-impact context with the team, and flag accounts where support patterns signal adoption or renewal risk.
• Define the metrics that matter. Establish and report on response and resolution times, SLA attainment, AI resolution rate, backlog health, CSAT, and case drivers, and turn recurring case themes into product, documentation, and onboarding improvements.
• Build the team. Develop the staffing and coverage model, decide where humans add the most value, and hire, onboard, and lead support engineers across regions as customer volume and global footprint grow.

• 8+ years in technical support, support engineering, or SRE/production engineering roles, including 3+ years leading a support function or team for a developer, infrastructure, observability, or B2B SaaS product.
• Experience building a support function from early stage: defining SLAs and severity models, standing up tooling, and designing 24x7 or follow-the-sun coverage for enterprise customers.
• Strong hands-on technical depth in production infrastructure: cloud platforms (especially AWS), Kubernetes, networking and authentication (SSO/SAML), and observability tools (e.g., Datadog, Grafana, Prometheus, Splunk, OpenTelemetry). You can dig into logs and reproduce a customer’s problem yourself.
• Active power user of AI agents and coding tools (e.g., Claude Code, Codex, Cursor) who has used them to automate real support or operations workflows. Comfortable with prompting, context engineering, and critically evaluating LLM output.
• A track record of handling high-severity customer incidents calmly, communicating clearly with both engineers and executives, and keeping customers informed when the answer isn’t known yet.
• Experience working closely with Engineering on defect triage and escalation, and with Sales and post-sales teams on account health.
• Data-driven: you define support metrics, build the reporting, and use it to drive decisions about staffing, product, and process.
• A strong sense of ownership. You hold a high bar for customer experience, you raise risks early, and you thrive in the ambiguity of an early-stage company where you build the playbook as you run it.

• Experience supporting AI/ML or LLM-based products.
• Experience with support for global enterprise customers, including contractual support terms and compliance requirements (e.g., SOC 2).
• Scripting experience in Python, TypeScript, or Go for support automation and tooling.
• Experience building self-service knowledge bases or developer documentation.