ML Research Engineer Intern – Dynamo Guard / Dynamo Eval / AgentWarden

Dynamo AI; New York (NY); On-site; USD 60,000 - 80,000; Full time

Job summary

A leading AI solutions provider in New York is looking for an ML Research Engineer Intern to design and implement innovative features across various platforms. The ideal candidate will have strong programming skills in Python and a solid understanding of machine learning principles. This role involves hands-on engineering, collaborating with research teams, and optimizing evaluation pipelines in a cloud-based infrastructure. If you're eager to build production-quality ML systems, this opportunity is for you.

Qualifications

• Currently pursuing or recently completed a degree in Computer Science, Machine Learning, AI, or related field.
• Strong programming skills in Python and familiarity with ML frameworks.
• Understanding of LLMs, agentic systems, or model evaluation techniques.

Responsibilities

• Design and implement ML-driven features across Dynamo Guard, Dynamo Eval, and AgentWarden.
• Build and optimize evaluation pipelines for LLMs and agentic systems.
• Develop scalable components within our Kubernetes-based infrastructure.

Skills

Python programming

Understanding of LLMs

Familiarity with ML frameworks

Problem-solving mindset

Experience with Docker/Kubernetes

Education

Currently pursuing or recently completed a degree in Computer Science, Machine Learning, AI, or related field

Tools

PyTorch

TensorFlow

Cloud environments

Job description

At Dynamo AI, an ML Research Engineer Intern will work at the intersection of machine learning research and production systems. You will help design, prototype, and deploy advanced evaluation, guardrailing, and agent security mechanisms across Dynamo Guard, Dynamo Eval, and AgentWarden. This role blends hands-on engineering with applied ML experimentation in real-world enterprise environments.

Responsibilities

• Design and implement ML-driven features across Dynamo Guard, Dynamo Eval, and AgentWarden.
• Build and optimize evaluation pipelines for LLMs and agentic systems, including attack simulations and red‑teaming frameworks.
• Develop scalable components within our Kubernetes-based infrastructure to support secure and reliable ML deployment.
• Translate research prototypes into production‑grade systems with strong performance and observability.
• Implement automated testing, benchmarking, and monitoring workflows for model safety and robustness.
• Collaborate closely with research and product teams to iterate quickly from experimentation to deployment.
• Contribute to internal tooling for model validation, guardrail enforcement, and runtime monitoring.

Qualifications

• Currently pursuing or recently completed a degree in Computer Science, Machine Learning, AI, or related field.
• Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
• Understanding of LLMs, agentic systems, or model evaluation techniques.
• Experience with cloud environments, APIs, or containerized systems (Docker/Kubernetes) is a plus.
• Strong problem‑solving mindset and eagerness to build production‑quality ML systems.