ML Research Engineer Intern – Dynamo Guard / Dynamo Eval / AgentWarden
Hybrid (New York, NY; San Francisco, CA; Remote)$3.5k/mo - $5k/moInternPosted 8mo ago
About the role
ML Research Engineer Intern at Dynamo AI working at the intersection of ML research and production systems to design, prototype, and deploy evaluation, guardrail, and agent security mechanisms across Dynamo Guard, Dynamo Eval, and AgentWarden in enterprise settings.
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.
Skills
Required:Python programmingML frameworksLLMs / agentic systems
Nice to have:Cloud / containerized environments
Last updated Oct 7, 2026. Interested? Apply on the company site.
About Dynamo AI
Compliant-Ready AI for the Enterprise
End-to-end privacy, security, and compliance solutions to prepare your organization for emerging AI regulations.
machine-learningprivacydata-engineering
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