Build safeguards and evaluations for conversational AI agents to prevent prompt injection, unsafe tool use, data disclosure, and hallucinations. This role requires 4+ years in AI/ML engineering or research, hands-on experience with language models and post-training techniques like reinforcement learning, and fluency in Python and modern ML tooling. You'll develop classifiers, red-team datasets, and runtime safeguards while analyzing production incidents to identify root causes. In-office position in the United States. $200K–$400K plus equity.
Written from this posting by Neural Jobs AI. The full description is below.
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 focused on Safety, you’ll be responsible for making Decagon’s AI agents safe, reliable, and controllable, from evaluation through production. You’ll identify real-world failure modes and build the models, evaluations, and safeguards that prevent them.
We’re looking for strong engineers who want to advance applied AI safety in production. 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
Research and build safeguards against prompt injection, unsafe tool use, sensitive-data disclosure, policy violations, and hallucinated commitments
Build adversarial evaluations, simulations, red-team datasets, and regression suites informed by production failures
Develop and deploy classifiers, judges, reward signals, post-training methods, and runtime safeguards for safer agent behavior
Analyze production traces and incidents to identify root causes, test mitigations, and measure their impact
Partner with Security, Product, Infrastructure, Legal, and customer-facing teams to turn enterprise requirements into scalable safeguards and rollout practices
Your background looks something like this
4+ years of experience in AI/ML engineering, research, or AI safety
Hands-on experience evaluating, post-training, or deploying language models or agentic systems
Experience with modern post-training techniques, such as reinforcement learning, preference optimization, distillation, model routing, and synthetic-data generation
Experience with adversarial testing, model red teaming, prompt injection, policy enforcement, privacy, or safe tool use
Fluency in Python and modern ML tooling, with strong experimental judgment and the engineering depth to ship production systems
Comfort owning ambiguous, high-stakes technical problems and making clear risk and product tradeoffs
Even better if you have
Experience building safeguards for high-stakes or regulated enterprise workflows
Familiarity with human-in-the-loop review, incident response, or responsible rollout frameworks for ML systems
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.
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Decagon develops AI customer-support agents for enterprise teams, resolving support conversations end to end across chat, email and voice.
Founded in 2023, the company builds agents that integrate with existing support tooling and workflows.
200K - 400K Yearly
United States
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