How to Get Hired at decagon

Decagon builds AI agents for enterprise customer support, helping companies automate and improve customer conversations using large language models. The company has grown quickly since its founding and, as of early July 2026, has 117 open roles spanning engineering, product, sales, customer success, and operations.

That number of openings signals a company in serious growth mode. Decagon hires across experience levels, but the common thread across roles is a bias toward people who move fast, take ownership, and are genuinely excited about applying AI to real business problems. The hiring process is structured but moves at startup speed.

Decagon's process typically runs four stages, though the exact sequence can shift by role. Treat the outline below as a general guide, not a guarantee.

Stage 1: Recruiter screen
A brief introductory call with someone from the talent team. You will cover your background, why Decagon interests you, and your current situation. The aim is a quick mutual check before the team invests more time. Come ready to speak clearly about why you want to work specifically on AI agents for enterprise customers.

Stage 2: Hiring manager or technical screen
You will speak with the hiring manager or a senior member of the team you are applying to. For engineering roles, expect a technical discussion or a short problem-solving exercise. For GTM and customer-facing roles, expect questions about how you have handled customers, driven metrics, and created outcomes in past jobs. The conversation is two-way, so prepare questions that show you have done your research.

Stage 3: Skills assessment
Depending on the role, this may be a coding challenge, a case study, or a short take-home project. Decagon is known for giving assessments that reflect real work, so focus on practical, clean solutions rather than clever tricks. For technical roles, demonstrating familiarity with LLM-based systems will stand out.

Stage 4: Final panel or interview loop
A loop with multiple team members covering technical depth, cross-functional thinking, and culture fit. Expect a mix of behavioral questions ('tell me about a time when...') and role-specific deep dives. This stage often includes someone from leadership.

Decagon is a small, high-output team building products in a fast-moving space, so the traits that matter most cut across all roles.

Genuine interest in AI, not just familiarity with it. Decagon builds at the frontier of LLM-powered agents. Candidates who have experimented with, built on, or deeply read about AI systems are consistently more competitive than those who treat AI as a buzzword.

Ownership mentality. With a lean team and a large number of open roles to fill, every person is expected to own a problem end to end. Interviewers will probe for examples where you drove something from start to finish without waiting to be told what to do next.

Customer empathy. Even for internal or engineering roles, Decagon's product ultimately serves enterprise customers. Understanding what customers actually need, not just what they say they need, is valued across the whole company.

Clear communication. Because the team moves fast, the ability to explain complex ideas simply, whether written or spoken, is a recurring theme in feedback from candidates who have gone through the process.

Relevant craft. For engineers, strong fundamentals and some exposure to ML or NLP systems is a meaningful advantage. For GTM and success roles, a track record of hitting targets and managing complex stakeholder relationships matters most.

Research the product before you apply. Decagon's website and publicly available case studies describe how their AI agents work and which enterprise clients they serve. Knowing this going in signals genuine interest and helps you tailor your answers.

Be ready to talk about AI concretely. Even for non-technical roles, interviewers are likely to ask how you think about AI, how you have used AI tools, or how AI might change your function. Have a point of view ready before your first call.

Prepare STAR stories focused on ownership and speed. Pick two or three examples from your career where you identified a problem, drove a solution, and delivered a result, ideally in a fast-moving environment. Keep each story tight: situation, what you specifically did, and the outcome.

For technical roles, practice applied problem-solving. Rather than drilling abstract puzzles alone, also practice building or reasoning about small AI-adjacent systems such as prompt design, retrieval pipelines, or evaluation frameworks. This reflects what Decagon actually builds.

Prepare thoughtful questions. Interviewers at early-stage companies pay attention to the quality of your questions. Ask about the roadmap, how success is measured in the role, or what the biggest unsolved challenge is for the team right now.

Apply broadly while you prepare. The market for AI roles is competitive. knok checks 150+ job sites every night, applies to roles that match your resume, and messages HR on your behalf, so you stay in the running while you focus on interview prep.

Decagon operates like a classic high-velocity AI startup. The team is small relative to the ambition of the product, which means each person carries significant responsibility and has real visibility into the business.

Speed and focus. Decagon ships fast and expects team members to match that pace. Decisions are made with available information rather than waiting for certainty, and iteration is the default mode.

Customer obsession. The product sits between Decagon, its enterprise clients, and those clients' end customers. Understanding what truly helps the end customer, not just what is technically impressive, is a core value that shows up in product decisions and in how the team communicates internally.

High hiring bar, collaborative environment. The company is selective in who it brings in, but the culture is commonly described by people who have worked there as collaborative rather than cutthroat. People are expected to help each other as well as own their individual areas.

Transparency. Early-stage AI companies tend to share context broadly so that the team can move in the same direction without constant top-down coordination. Decagon fits this pattern, with founders and leadership staying visible and accessible to the broader team.