Product Analyst

About Dynamo AI

Dynamo AI builds the platform enterprises use to bring AI applications to production and keep them there. Our products cover risk assessment, evaluation, runtime guardrails and observability for generative AI and agentic systems. We work with enterprises in regulated industries, including financial services, insurance, healthcare and defense.

Our PMs each own a set of business problems, such as getting AI agents approved for production, reducing the effort legal and compliance teams spend on guardrails, or lowering the cost of running guardrails at scale. Almost every one of these problems depends on evidence: whether an approach works, how well, and at what cost. Product Analysts produce that evidence.

Working with the PM who owns a problem, you take a specific question within it and own the answer. You scope the question, design the experiment or prototype, define how success is measured, run the analysis, and come back with a recommendation the PM can act on. When building is faster than waiting, that includes standing up a quick tool with AI to test an idea. You may support several PMs as priorities shift.

Training and infrastructure code is handled by our engineering and research teams. Your contribution is experimental design, prototyping and analytical rigor.

• Guardrail creation and upkeep: design and curate the data used to train and accept custom guardrails, keep labeling consistent as datasets grow, and measure whether updates made from production errors improve performance without regressions.
• Control coverage: benchmark detection against new attack techniques, languages and modalities, and scope what closing each gap would take.
• Evaluation evidence: assess whether synthetic evaluation and red-teaming data is faithful and diverse enough for customers' approvers to trust, and research new ways to evaluate AI applications and agents.
• Cost and performance: compare guardrail and orchestration configurations on accuracy, latency and inference cost.
• Alerting: prototype ways to aggregate and prioritize guardrail alerts, and measure their effect on the workload of security analysts.
• Workflow prototypes: build and test prototypes of new workflows, such as how legal and compliance reviewers contribute to guardrail policies or how customers onboard an application on their own, and measure the time and effort they take.
• Rapid tooling: build small custom tools with AI to validate a hypothesis or unblock the team.
• Benchmarking and reporting: measure performance with standard classification metrics (FNR, FPR, precision, recall) and report what is working and what is not.

You will start on well-scoped questions and move to harder, more ambiguous ones. Over time you will build intuition for SLM training, guardrail orchestration, agent security and how controls fit into a full AI application, along with an understanding of how our customers make approval decisions. This is a track toward a Product Manager or senior technical product role.

• A degree with strong quantitative or analytical content. We are especially interested in less common backgrounds, such as physics, information or data theory, and business, alongside solid data analysis experience.
• A clear, demonstrated framework for thinking through problems.
• Good instincts 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 in adversarial thinking.
• Attention to detail and clear writing, including the ability to turn results into a recommendation a PM can act on.

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

• Produce the evidence behind product decisions across every Dynamo product.
• Work on AI security and governance problems that enterprises are facing for the first time.
• Grow toward a product role at a founder-led startup.
• Competitive compensation, equity and benefits.