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

Dynamo AI; San Francisco (CA); On-site; USD 34,440 - 55,104; Full time

Job summary

A leading AI research company located in San Francisco is seeking an ML Research Engineer Intern. This role focuses on designing and implementing ML-driven features, optimizing evaluation pipelines, and collaborating with cross-functional teams. The ideal candidate is pursuing or has completed a degree in Computer Science or related fields, possesses strong Python programming skills, and is eager to work on production-quality ML systems. This internship will provide hands-on experience in a dynamic enterprise environment.

Qualifications

• Currently pursuing or recently completed a degree in a relevant field.
• Strong programming skills in Python.
• Familiarity with ML frameworks like PyTorch or TensorFlow.

Responsibilities

• Design and implement ML-driven features across our platforms.
• Build and optimize evaluation pipelines for LLMs.
• Collaborate with research and product teams to improve deployments.

Skills

Python programming

Machine Learning frameworks

Problem-solving

Education

Degree in Computer Science, Machine Learning, AI

Tools

Docker

Kubernetes

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.