About DynamoAI

Dynamo AI is building the future of trustworthy AI for the enterprise. Our platform provides real-time guardrails, redteaming, and observability for generative AI systems—ensuring safe, compliant, and reliable AI deployments in regulated industries such as financial services, insurance, DoD, and healthcare.

The role

PMs run several workstreams at once. As an analyst, you take individual complex items inside those streams and own their design and specification: scope the problem, design the experiment, define how success is measured, run the analysis, and come back with a recommendation. When building is faster than waiting, that includes standing up a quick custom tool with AI to test an idea.

You won't write training or infrastructure code; the platform handles that. What you bring is experimental design and analytical rigor.

What you might work on

Guardrail training data: curate and design the data that trains an SLM guardrail (for example, a prompt injection detector), and keep labeling consistent as datasets grow.
Orchestration experiments: test different configurations of guardrails and controls against each other to find what performs best.
Synthetic data quality: assess whether synthetic training or eval data is faithful and diverse enough to trust.
New evaluations: research and scope new ways to evaluate guardrails and AI applications, then specify how they work.
Rapid tooling: build small custom tools with AI to validate a hypothesis or unblock the team.
Benchmarking: measure performance with standard classification metrics (FNR, FPR, precision, recall) and report what's working and what isn't.

Growth path

You'll start on well-scoped items and move to harder, more ambiguous ones. Over time you'll build intuition for SLM training, guardrail orchestration, and how controls fit into a full AI application. This is a track toward a senior, technical product role.

Requirements

A degree with strong quantitative or analytical content. We're especially interested in less common backgrounds (physics, information or data theory, business, and similar) alongside solid data-analysis experience.
A clear, demonstrated framework for thinking through problems.
Good instinct for data, ML concepts, and what makes an experiment or metric trustworthy.
Python and pandas for working with data.
Interest in AI security or safety and adversarial thinking.
Attention to detail and clear writing.

Nice to have

ML coursework.
Reading or working proficiency in Japanese, Chinese, or a European language.
Coursework or projects in stats or NLP.
SQL.
A habit of building small tools or scripts (including with AI assistants) to answer your own questions.
Exposure to LLMs, security tooling, experimental design, or annotation work.

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