About the Role We are looking for someone to join us in redefining how engineers and researchers work with AI, taking ownership of understanding the problem space and shaping the roadmap with evidence. You'll take full ownership of translating research-stage, ambiguous technical work into a clear, prioritized backlog that a team can actually execute against. You should be comfortable diving into technical detail and passionate about making complex, research-heavy workflows feel tractable, not just for the team building the product, but for the customers relying on it. This isn't a role for someone who needs a roadmap handed to them fully formed. You'll be shaping What You'll Do ● Own and groom the product backlog for a scientific/ML engineering team, translating research-stage work into features that are estimable and sequenced, without stripping out genuine ambiguity ● Work closely with the research and engineering team to understand their workflows and pain points well enough to represent them accurately in product decisions ● Build and defend a prioritization approach, starting with a foundational model for the initial release and evolving it as real usage data comes in ● Balance long-term roadmap thinking with the immediate needs of the team and customers, navigating ambiguity without losing sight of the bigger picture ● Set up and run the team's working process and tracking, comfortable building the system rather than inheriting one ● Take on defined market research activities such as competitive landscape scans, customer and segment validation, and positioning input, feeding directly into roadmap decisions ● Produce backlog and roadmap artifacts that are self-documenting and usable by both the technical team and cross-functional stakeholders Must have: ● Product Owner/Manager experience in a B2B enterprise software product, not a consumer or internal-tools context ● A deep understanding of how engineers or technical researchers actually work: their workflows, pain points, and what "done" looks like from experimentation through deployment ● A structured, strategic mindset, with the ability to navigate ambiguity, drive alignment, and deliver value quickly without losing sight of long-term goals ● Direct experience with on-premise or hybrid deployment models, and the constraints these place on architecture, rollout, and customer expectations beyond pure SaaS ● Exposure to PLM (Product Lifecycle Management) ecosystems or workflows, familiar with how engineering data and processes are governed in that world ● A bias toward action, with a track record of resolving blockers and iterating quickly in a fast-moving environment Good to Have ● Experience building products for technical users such as ML engineers, data scientists, or applied researchers ● Background in AI platforms or scientific computing environments ● Exposure to CAE or 3D visualization tools within engineering software ecosystems ● Familiarity with data governance, sovereignty, or compliance considerations in enterprise B2B products ● Enthusiasm for product management as a craft, with clear opinions on how to build successful products