Senior Software Engineer, Product

Decagon
New York, NY
On-site

Who this role is best for

Geared toward candidates with product engineering experience comfortable with building AI-powered agent systems.

Best fit for

  • Candidates with product engineering experience and a background in AI agent systems or LLMs
    — “Background with LLMs, prompt engineering, evals, or AI agent systems”
  • Individuals who excel at designing scalable systems and translating technical complexity into user-friendly interfaces
    — “Design configuration abstractions that balance power and simplicity, handling diverse enterprise use cases while staying usable by non-technical teams”
  • Candidates who thrive in collaborative environments, working closely with product and design teams
    — “You’ll work day to day with product and design: joining discovery, defining requirements, reviewing designs, and iterating through launch”

Things to consider

  • In-office work is required, with no mention of remote flexibility
    — “We’re an in-office company, driven by a shared commitment to excellence and velocity”
  • The role involves handling critical AI systems for Fortune 100 companies
    — “Handle agentic systems at scale; your work will touch critical flows for Fortune 100 companies”

How to stand out

  • Highlight experience with multi-modal AI systems and observability tools in your resume and interviews
    — “Experience with observability, data visualization, or analytics products”
  • Showcase your ability to build self-serve tools for non-technical users
    — “Eliminate manual work by building self-serve functionality for agent configuration, training, and deployment”
  • Emphasize your track record in debugging deep technology stacks and handling system failures
    — “A high degree of comfort debugging system failures in deep technology stacks, using whatever tool gets you to the answer”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design and build self-serve functionality for agent configuration, training, and deployment
  • Build monitoring, tracing, and analytics that surface actionable signal
Typical background
4+ years building software productsExperience translating complex technical capabilities into simple, usable interfaces

Skills & requirements

Required

PythonProduct ThinkingComplex Systems InstinctsAsynchronous Programming

Preferred

AIMonitoring And Tracing

Stack & domain

PythonLeadershipCommunicationFinanceHealthcare

About the role

Original posting from Decagon via Ashby

About Decagon

Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.

Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.

We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.

We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.

About the Team

Product Engineering at Decagon builds all aspects of the platform our customers use to create, operate, and improve their AI agents. We turn work that currently requires an engineer into product that a CX team can run themselves.

This team is core to how Decagon scales. The frameworks we build support every agent we have in production, each with its own workflows, data, and constraints. We build the agents too, and that’s what keeps the platform honest: a failure mode we debug in production becomes a guardrail, an eval, or a piece of tooling that catches it everywhere.

Our agents run for companies like Hertz, American Airlines, and Notion across chat, voice, email, and SMS.

About the Role

You’ll design and build the systems that let customers configure, train, test, and monitor sophisticated AI agents with confidence. That means strong engineering underneath, and abstractions on top that hold up across different use cases without forking per customer.

You’ll work day to day with product and design: joining discovery, defining requirements, reviewing designs, and iterating through launch. You’ll stay close enough to customers to understand their use cases in depth and to ensure what you’re building is actually right for them.

Engineers here own features end to end, from technical architecture through deployment and the iteration after it.

In this role, you will

  • Eliminate manual work by building self-serve functionality for agent configuration, training, and deployment
  • Design configuration abstractions that balance power and simplicity, handling diverse enterprise use cases while staying usable by non-technical teams
  • Build AI-powered tooling that lets CX and ops teams create and manage complex agent workflows without writing code
  • Build monitoring, tracing, and analytics that surface actionable signal, so customers can see where an agent is failing and why
  • Handle agentic systems at scale; your work will touch critical flows for Fortune 100 companies.
  • Run evaluations on new text and voice models, then integrate the ones that win into production traffic

Your background and skillset look something like this

  • 4+ years building software products, with strong product thinking
  • Proficient in Python, comfortable working across the entire stack
  • Experience translating complex technical capabilities into simple, usable interfaces
  • Complex systems instincts, including asynchronous programming and the failure modes that only appear at scale
  • A high degree of comfort debugging system failures in deep technology stacks, using whatever tool gets you to the answer
  • A knack for turning customer asks into clear, well-scoped product and technical specs
  • Judgment about when to ship fast and when to slow down and build it correctly

Even better

  • Background with LLMs, prompt engineering, evals, or AI agent systems
  • Experience building developer tools, APIs, platforms, or internal tools that became customer-facing
  • Experience with observability, data visualization, or analytics products
  • Experience building security-sensitive product systems, such as authentication and data access controls
  • Work with multi-modal or voice models

Compensation

$200K – $400K + Offers Equity

This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact.

In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States.

Benefits

We proudly offer the following benefits for our full-time employees:

  • Medical, Dental, and Vision benefits for you and your family
  • Life Insurance and Disability Benefits
  • Retirement Plan (e.g., 401K, pension)
  • Parental Leave
  • Fertility and family building benefits through Carrot
  • Monthly stipend to support your wellness, lifestyle, and work-life balance
  • Daily lunches and snacks in the office to keep you at your best
  • Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)

These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.

Source: Decagon careers (Ashby)

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