Decagon — Customer Engineer, Agent Builder (NYC)

Type: Full-time | On-site | New York City, NY Compensation: $175,000–$230,000 + competitive equity Hiring count: 2 Visa sponsorship: Yes — H-1B Reports to: Not specified on role page (partners with Agent PMs, Agent Success, Engineering, and Go-To-Market)

About Decagon

Decagon is the leading conversational AI platform, enabling enterprises to deploy AI agents that handle customer interactions across voice, chat, email, SMS, and every other channel — replacing support tickets and hold music with faster resolutions and richer conversations. Public customers include Avis Budget Group, Cash App and Square, Chime, Oura Health, and Hunter Douglas. It is an in-office company built around velocity and a shared commitment to excellence, with values of Just Get It Done, Invent What Customers Want, Winner's Mindset, and The Polymath Principle.

Founded: 2023 | Team size: 201–500 (Series D+) | Total funding: not disclosed on role page Investors: a16z, Accel, Bain Capital Ventures, Coatue, Index Ventures Industry: Conversational AI · Customer Support · Enterprise SaaS Website: https://decagon.ai Office: New York City, NY

Why Candidates Should Join

• Ground-floor on a new org: The Agent Builder team is newly formed — you help shape how Decagon's agents are built, validated, and delivered, and feed real customer needs back into the platform.
• Genuine technical depth, not slideware: Roughly a 50/50 split of hands-on build work and customer-facing delivery; you own agents end-to-end from systems design through production.
• Marquee backing and customers: Backed by a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures; deployed at Avis Budget Group, Cash App/Square, Chime, Oura, and Hunter Douglas.
• Comp and perks: $175–230K + competitive equity, take-what-you-need vacation, full medical/dental/vision for you and family, life + disability, retirement plan, parental leave, Carrot fertility/family-building benefits, and daily lunches and snacks in-office.

Intake Call Summary

• No intake call transcript was included in the pasted HTML. An intake video is present on the role page but was not transcribed — pull it separately if a summary is needed.

The Role

A post-sales, forward-deployed engineer who owns end-to-end execution of AI agent builds for strategic enterprise customers — roughly a 50/50 split of hands-on technical build work and customer-facing delivery, on a newly formed Agent Builder team. Ideal for a software or forward-deployed engineer who wants to pivot into customer-facing delivery and thrives in fast-moving, ambiguous environments.

What You'll Be Doing

• Own end-to-end architecture and execution of AI agent builds for enterprise customers, from systems design and scoping through implementation, evaluation, and production deployment
• Design and engineer the core agentic logic governing agent behavior, plus layered guardrails and supervisory controls for safe, compliant, predictable performance across non-deterministic outputs
• Architect, build, and test integrations with customer systems (data pipelines, CRMs, ticketing systems), including the tools, APIs, and workflows needed for reliable deployment at scale
• Interface with senior technical stakeholders at customers to define success criteria and system requirements, and drive technical delivery against timelines
• Diagnose, debug, and resolve probabilistic and technical failures through root-cause analysis, and design evaluation and regression-testing frameworks that guard against performance drift

Tech stack: Python (primary; assessed via a live Python round and case study), REST APIs, LLM / agentic tooling, enterprise integrations (CRMs, ticketing systems, data pipelines)

Requirements

• 3+ years in a technical, customer-facing role (solutions engineering, forward-deployed engineering, technical consulting, implementation engineering, or technical product)
• Strong Python proficiency — comfortable writing code, working with APIs, and building and validating integrations end-to-end (assessed via a live Python round and case study)
• Genuine post-sales interest — wants to go deep on hands-on technical delivery and customer engagement, not pivot toward pre-sales
• Exceptional communication skills and executive-ready presence, credible with senior technical stakeholders
• Comfort working in fast-moving, ambiguous environments, shaping solutions as much as implementing them, and available in-office 5 days per week

Green Flags

• Experience building with or around LLMs and AI agents (prompting, evaluation, guardrails, tooling, workflow design)
• Experience with enterprise SaaS integrations (ticketing systems, CRM, data pipelines) and associated security and compliance considerations
• A Computer Science, Engineering, or Math degree, or equivalent technical background (CS strongly preferred; other quantitative degrees such as Data Science or Data Engineering acceptable)
• Strong product instinct: crisp PRDs, clear success metrics, and customer insight fed back into the product roadmap

Red Flags

• Background as an IT manager or in IT operations without transferable hands-on technical build experience
• Currently at Cognition or Sierra (do not contact due to existing relationships)
• Currently at C3 AI or Amazon (historically weak fit in this process)
• Interested only in pre-sales work; this role's lane is exclusively post-sales

Role Details

• Salary — $175,000–$230,000
• Equity — Competitive equity
• On-site policy — In-office 5 days per week
• Visa sponsorship — H-1B
• Employment type — Full-time
• Location — New York City, NY
• Hiring count — 2

Screening Questions

Contrario's Required Candidate Q&A — completed on the submission form; several also feed the submission table.

1. Phone Number
2. LinkedIn
3. What college/university did you attend?
4. Are you legally authorized to work in the United States?
5. Will you now or in the future require visa sponsorship to work in the United States?
6. Are you excited to work in-office five days a week?
7. How did you hear about Decagon?

Interview Process

Stage 1 — Recruiter / HM Screen — Initial recruiter or hiring-manager conversation. Stage 2 — Initial Screen — First-round evaluation. Stage 3 — Take Home — Take-home exercise. Stage 4 — Onsite — Includes a live Python round and a case study. Stage 5 — Debrief — Internal panel debrief. Stage 6 — Reference Check — References. Stage 7 — Closing — Closing conversation. Stage 8 — Offer Extended Stage 9 — Candidate Hired — Candidate accepts and starts.