Description
Complex support cases do not require another person following scripts. They require someone capable of navigating unclear context, misconfigured systems, conflicting evidence, upset customers, and AI-generated noise—then determining what actually went wrong. This position is designed for the support engineer drawn to the unresolved ticket: replicating the issue, analyzing logs, examining API behavior, inspecting JSON payloads, distinguishing a 401 from a 404 from a 429, and leveraging AI aggressively without accepting its output uncritically.
Most support organizations still rely on ticket queues, templated responses, handoff procedures, and documented issue libraries. We are building something fundamentally different. Routine inquiries are increasingly addressed by AI and L1 processes, which means human expertise is allocated to the hardest challenges. AI is not here to replace you. It is the instrument you command, anchor, scrutinize, and validate.
This role is not about copying AI responses, making quick guesses, waiting for direction, or escalating as soon as the problem becomes unfamiliar. It is not suited for someone comfortable with one product who stalls when the following ticket involves a different technology stack. It is about delivering the correct answer with the minimum customer interactions the situation permits: reproducing before suggesting solutions, investigating across tickets, Slack channels, knowledge bases, logs, configurations, and actual evidence before escalating, and crafting customer communications precise enough to calm the situation. The technical foundation is assumed: REST APIs, JSON, HTTP status codes, command line interfaces, and log analysis. That qualifies you for entry. Sound judgment amid ambiguity is what makes you effective.
In this position, you own each issue until resolution or clear escalation, providing diagnostic logic valuable to the subsequent agent and the next AI process. You will thrive in this role if the ticket that defies explanation is the one you pursue most eagerly. You will struggle if you require a single product, a predefined playbook, and external help to move forward. If that environment energizes you, we encourage you to apply.
What you will be doing
- AI-Augmented Customer Resolutions: Resolving ambiguous, escalated tickets by merging rigorous, practical troubleshooting with AI that you control, anchor in verified documentation, and confirm.
What you will NOT be doing
- Spending two full months onboarding; you will be required to become proficient with one or more products in the first month (we recognize this pace is demanding)
- Depending on managers for assistance; if you are not skilled at self-unblocking, this role will likely pose significant challenges
Key responsibilities
- Combine technical human judgment with AI capabilities to provide outstanding customer support, concentrating on complex problems that AI cannot yet independently resolve
Candidate requirements
- 2+ years in a practical technical position such as technical support, customer support engineering, software engineering, QA, or sysadmin/DevOps. The job title need not say "support."
- Proficient in creating and interpreting REST API requests and JSON, understanding HTTP status codes (such as 401 vs 404 and 429 vs 403), and operating within a command line interface (CLI) and logs.
- Practical experience applying generative AI tools (such as ChatGPT or Claude) in daily technical responsibilities.
- Professional-level fluency in English, both written and verbal.
- Available to work full-time from 1:00 PM – 10:00 PM UTC (8:00 AM – 5:00 PM US Eastern)
Nice to have
- Background supporting technical or business clients on enterprise software or B2B SaaS, beyond internal IT.
- API debugging using tools such as Postman or curl.
- Demonstrated ability troubleshooting across multiple unrelated products or technology stacks, not confined to one.
- Developer-level expertise: capable of reading code or tracing an API call when the situation requires it.
Applying for a role? Here’s what to expect.
Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.
Chat-style screening interview.
Cognitive aptitude test.
Prove real-world job skills.
Interview with the hiring manager.
Pass proctored test.
Accept job offer.
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