Research Engineer

Decagon is hiring a Research Engineer in San Francisco, United States. It pays $200k-$400k a year and Level rates it ; you can apply on Decagon.

Level's read on this job

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include AI agents, machine learning and conversational AI.
Level classifies it as a mid-level role.
Level lists 133 other open roles at Decagon.

AI in this role

Research Engineer building state-of-the-art conversational AI models and decision-making stacks for enterprise support agents.

ai-agentsmachine-learningconversational-aimodel-trainingprompt-engineering

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

Read more about the research team's work here: https://decagon.ai/blog/introducing-decagon-labs

The Research team develops the model and decision-making stack that powers Decagon’s conversational agents for enterprise support. We research, adapt, and implement state-of-the-art techniques in model training, prompting, orchestration, and evaluation in order to make our agents more accurate, robust, and efficient in real-world deployments.

Our goal is to push the frontier of applied conversational AI: agents that reliably understand nuanced intent, track long context, and take the right actions under uncertainty. We measure success the way customers feel it: higher resolution rates, better user satisfaction, and consistent behavior at scale.

About the Role

As a Research Engineer, you’ll be responsible for building industry-leading conversational AI models that power Decagon’s agent, and taking them all the way from idea to production. You’ll own multi-quarter initiatives that push the agent’s reliability, capability, and efficiency forward, and you’ll design and implement frontier approaches for training, evaluation, and orchestration across the system.

We’re looking for strong engineers who want to build the core models and algorithms behind our AI agents. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions.

In this role, you will

Lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, retrieval, memory, and long-horizon task completion
Build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience
Partner with platform and product engineers to integrate new models into production systems
Break down ambiguous research ideas into clear, iterative milestones and roadmaps.

Your background looks something like this

2+ years of experience in AI/ML engineering or research.
Prior experience post-training and deploying LLMs in production environments.
Fluency in Python and modern ML tooling (training, evaluation, data pipelines)
Track record of taking research ideas from prototype → reliable, measurable production impact

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.

How we rate this

Research Engineer at Decagon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Research Engineer is an open job at Decagon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $200k-$400k a year.
The role is based in San Francisco.
The job was posted on July 22, 2026.
Seniority: mid.
Applications are handled by Decagon. Apply on Decagon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsMachine learningConversational AIModel TrainingPrompt engineering

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where conversational ai was part of your work. What did you do?
Tell me about a project where model training was part of your work. What did you do?
How do you structure and test a prompt to get consistent output from a language model?

Adapt your resume

List these exact terms on your resume: AI agents, Machine learning, Conversational AI, Model Training, and Prompt engineering. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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