Product Analyst

### 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.