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

Job in New York, New York County, New York, 10261, USA
Listing for: Dynamo AI
Apprenticeship/Internship position
Listed on 2026-06-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below
Position: ML Research Engineer Intern – Dynamo Guard / Dynamo Eval / AgentWarden
Location: New York

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, guard railing, and agent security mechanisms across Dynamo Guard, Dynamo Eval, and Agent Warden. 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 Agent Warden.
  • 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, Tensor Flow).
  • 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.
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