Product Management Lead, AI
Role Overview
Magnet Forensics is building the next generation of AI-native, agentic capabilities across our digital forensics and incident response (DFIR) portfolio. This role is your opportunity to define and ship agentic experiences that change how investigators, examiners, and analysts do their most important work.
As Product Management Lead, Agentic AI, you will drive the strategy and execution for agentic product experiences across the Magnet portfolio. You will turn fast-moving AI capabilities into trusted, governed products across the entire Magnet One platform that delivers measurable value, partnering closely with customers, engineering, design, data science, and go-to-market teams. You have already built agentic experiences elsewhere, and you will help us do the same here, at the scale and trust bar that forensic work demands.
This is a high-impact individual contributor role for a builder who is energized by frontier AI, comfortable with ambiguity, and focused on adoption, growth, and outcomes that show up in the data.
What You'll Do
- Drive the agentic strategy: Drive the product vision, roadmap, and success metrics for agentic experiences across the portfolio, from focused agent workflows to full orchestration.
- Build agentic experiences that ship: Design and deliver AI-native products (agents, copilots, human-in-the-loop workflows, tool use, retrieval, and reasoning) that are reliable, explainable, and production grade.
- Make autonomy trustworthy: Treat governance as an enforced constraint, not an aspiration. Define hard limits, output verification, reversibility, escalation paths, and auditability so users can trust what an agent does on their behalf.
- Drive product adoption for AI: Own activation, onboarding, and the path to habitual use. Reduce friction, design for time to value, and grow adoption across new customers, existing customers, and internal teams.
- Drive growth: Build and instrument growth loops, expansion, and retention. Partner with go-to-market and growth to turn capability into usage, usage into value, and value into revenue.
- Decide with data: Instrument every experience, define the metrics that matter, and run a disciplined experimentation practice (A/B tests, funnels, cohort analysis) so decisions are grounded in evidence, not opinion.
- Run continuous discovery: Engage customers and prospects through interviews, onsite visits, workshops, and discovery sessions. Map opportunities to solutions, validate before building, and keep a living view of unmet needs.
- Master the market: Develop deep expertise in DFIR, the agentic AI landscape, competitive dynamics, and emerging model and tooling trends to inform strategic bets.
- Navigate competing priorities: Make thoughtful tradeoffs across scope, quality, trust, and timelines while staying aligned to strategic objectives.
- Drive clarity: Break ambiguous problem spaces into structured, actionable plans that align leadership and let teams execute with confidence.
- Lead strategic visibility and alignment: Track the metrics that signal success and give clear, outcome-focused updates to executives and cross-functional partners.
- Collaborate across teams: Partner with engineering, UX, data science, and peer PMs to deliver experiences that are intuitive, consistent, and aligned to key personas.
- Influence cross-functionally: Build strong relationships with GTM, customer success, support, and leadership to drive alignment and optimize results.
- Work AI-natively: Default to AI across your own workflow (discovery, research, analysis, drafting, and prototyping) and model AI-native ways of working that raise the bar for the wider product team.
- Travel: 5-15%.
What We’re Looking For
Agentic AI experience (required): You have built and shipped agentic or AI-native product experiences (agents, copilots, autonomous or semi-autonomous workflows) in production. You can speak concretely to what you shipped, what worked, and what did not.
AI-native operator (required): You use AI as a default part of how you work, not an occasional assist. You are fluent with current AI tools and prompt engineering for research, discovery synthesis, competitive analysis, drafting (PRDs, specs, user stories), data analysis, and rapid prototyping.
Experience: 7-10 years in product management, ideally with enterprise SaaS, including meaningful time on AI or ML-powered products.
Adoption and growth: Demonstrated track record driving product adoption, activation, retention, and growth, ideally in a product-led motion.
Data-driven decision making: Fluent with product analytics and experimentation. You instrument what you build and let metrics guide tradeoffs.
Product discovery: Skilled in continuous discovery and validation techniques (customer interviews, opportunity-solution mapping, Jobs To Be Done).
Technical acumen: Able to partner closely with engineering and data science on AI system design (models, retrieval, evaluation, latency, cost) and to reason about tradeoffs across complexity, scalability, trust, and customer value.
Analytical mindset: Skilled at interpreting market data, customer insight, and usage metrics to drive decisions.
Influence and communication: Strong collaboration skills and the ability to communicate effectively with both technical teams and executive stakeholders.
Ownership mindset: Act as the champion for your product, anticipating challenges, removing obstacles, and driving momentum to meaningful business results.
Nice to Have Skills
Industry experience in Digital Forensics, Incident Response, cybersecurity, or other regulated, high-trust domains.
Hands-on experience with modern AI tooling and patterns (LLM orchestration, RAG, agent frameworks, evaluations, guardrails).
Familiarity with AI governance, safety, or responsible AI practices in a product context.
- Experience building within On-Premise and Hybrid Deployment models.
Experience with Pragmatic Marketing, Design Thinking, Jobs To Be Done, or Continuous Discovery, or equivalent.
Comfort building working prototypes or demos with AI app builders and coding copilots.
Experience in Agile development and familiarity with product and analytics tooling including Jira, Aha, and Amplitude.