Research Engineer: Production AI (Equity)

Doist; New York (NY); On-site; USD 200,000 - 400,000; Full time

Benefits offered by this job

Take what you need vacation policy

Medical, Dental, Vision benefits

Life Insurance and Disability Benefits

Retirement Plan (401K)

Parental Leave

Fertility and family building benefits

Daily lunches and snacks

Job summary

Decagon is seeking a Research Engineer to build industry-leading conversational AI models powering its enterprise agent. You’ll own multi-quarter initiatives to improve reliability, capability, and efficiency, and design frontier approaches for training, evaluation, and orchestration across the system.

You will lead end-to-end model development, ship real improvements, and make high-impact technical decisions.

Qualifications

• 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

Responsibilities

• 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.

Skills

AI/ML engineering

Production deployment

Python

ML tooling

Research to production

Job description

Decagon is seeking a Research Engineer to build industry-leading conversational AI models powering its enterprise agent. You’ll own multi-quarter initiatives to improve reliability, capability, and efficiency, and design frontier approaches for training, evaluation, and orchestration across the system.

You will lead end-to-end model development, ship real improvements, and make high-impact technical decisions.