Technical Product Manager

Job Title: Technical Product Manager Location: Austin, TX / Cupertino, CA (Hybrid) Duration: 2 Months Job Summary We're looking for an exceptional Technical Product Manager to support the development of Expert - an AI/ML-powered decision support web application transforming how Channel Sales teams surface insights and act on data. You will sit at the intersection of AI/ML, data, and business strategy - translating complex technical capabilities into elegant, impactful product experiences. You bring deep technical fluency, strong product instincts, and the ability to earn the trust of world‑class engineers, data scientists, and business stakeholders alike. Description Serve as the unifying thread between business strategy and engineering execution - anchoring user needs and business goals while partnering with ML engineers, full‑stack developers, data scientists, and designers to build and scale Expert. Support the product vision, strategy, and multi‑year roadmap - from AI/ML capabilities and data foundations to user‑facing features. Author clear, detailed product and technical specifications that give engineering and data science teams everything they need to build with confidence. Support the design of Expert's AI‑powered features: query understanding, intelligent retrieval, answer generation, confidence signaling, and feedback loops. Define and monitor success metrics spanning both product and technical outcomes. Communicate product strategy, roadmap, and technical trade‑offs clearly to stakeholders at all levels, from engineering teams to senior leadership. Partner with Sales, Finance, Product Marketing, Legal, and IS&T to deliver a cohesive and impactful product experience from end to end. Responsibilities Drive product and technical vision - Support Expert's roadmap from AI/ML investments to user‑facing features. Define the product strategy, not just the feature backlog. Create PRDs, translate into Epics, stories, and tasks, while participating in all agile ceremonies to execute. Lead AI/ML feature strategy - Translate business problems into well‑defined AI/ML opportunities. Define success criteria, data requirements, and evaluation frameworks. Partner closely with ML engineers and data scientists to guide features from concept to production. Simplify the complex - Distill intricate technical and strategic concepts into clear, persuasive language for diverse audiences - translating trade‑offs up to leadership and business context down to engineering teams. Advocate for the user - Lead discovery with Channel Sales teams through qualitative research, usability testing, and quantitative usage analysis. Ground every investment in demonstrated user need and bring that evidence into technical discussions. Keep business tied to all decisions - Define product KPIs and leverage data to drive independent analysis and informed prioritization. Design the instrumentation layer that enables experimentation and outcome measurement. Build trust through depth - Engage in technical discussions and write specifications at a level that earns genuine credibility with engineering and data science partners. Minimum Qualifications Strong technical background with hands‑on experience in AI/ML product development, data‑intensive platforms, or software engineering. 5+ years of product management experience, including 3+ years on AI/ML products or data‑intensive platforms shipped at scale. Hands‑on experience shipping production AI/ML features – including defining success criteria, supporting deployment, and measuring outcomes. Demonstrated experience with LLM‑powered products: built or shipped at least one feature leveraging LLMs, RAG, or generative AI in a production context. Data fluency; comfortable working directly with data to explore, analyze, and support independent decision‑making. Experience writing clear, detailed product and technical specifications that engineering teams can build from directly. Demonstrated ability to engage meaningfully in technical planning and design discussions, and to evaluate engineering trade‑offs. Track record of owning both product outcomes and AI/ML performance metrics simultaneously. Strong written and verbal communication skills with the ability to flex between highly technical and executive audiences. Preferred Qualifications MS or PhD in Computer Science, Machine Learning, Data Science, NLP, or related technical field. Familiarity with agentic AI concepts and emerging AI system design patterns. Background in enterprise sales analytics, revenue intelligence, or channel partner ecosystems. Familiarity with vector databases and semantic search systems. Experience defining or working with LLM evaluation approaches and quality measurement. Familiarity with data pipeline orchestration, streaming architectures, and real‑time feature serving. Data‑driven decision maker - Comfortable working directly with data to surface insights, validate hypotheses, and make informed product decisions independently. Healthy skeptic of AI - Has strong intuitions about when ML is – and isn't – the right solution; pushes back on AI-for-its-own-sake and holds GenAI work to rigorous success criteria. Systems thinker - Considers how decisions ripple across system performance, reliability, and scale. Calm under pressure - Brings clarity and steadiness in fast‑moving situations. Strong communicator - Explains technical ideas clearly and credibly to both engineering teams and senior leadership. Education BS/MS in Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical discipline – or equivalent experience. #J-18808-Ljbffr