Product Manager

Responsibilities

• Define scope and sequence: Partner with Engineering to turn foundation model capabilities (aiCast, aiMatch) into well-scoped, shippable enterprise product phases.
• Own the delivery timeline: Lead execution alignment, ruthlessly prioritizing the product backlog to ensure delivery of the right features with the right technical scope at the right time.
• Bridge research and commercialization: Act as strategic connective tissue between engineering milestones and customer needs, translating technical limitations or breakthroughs into predictable roadmap timelines.
• Optimize the model lifecycle: Collaborate with ML platform engineers to optimize model onboarding, evaluation pipelines, and user-facing feedback loops for frictionless deployment.
• Facilitate cross-functional execution: Coordinate with Frontend, Design, and Applied AI teams to ensure seamless integration into the core platform and customer solutions.

Requirements

• 5+ years of Product Management experience in highly technical domains such as AI platforms, developer tools, MLOps, or complex enterprise data infrastructure.
• Engineering-first mindset: Respect and understand deep technical architecture; comfortable challenging and being challenged by engineers on technical scope, trade-offs, and dependencies.
• Technical data fluency: Strong comfort with mechanics of structured/time-series data, model evaluation metrics (e.g., precision, recall, error rates), and data pipeline architectures.
• Systems thinking and scoping mastery: Proven ability to break down highly complex, ambiguous technical projects into logical, incremental releases without slowing engineering momentum.
• Exceptional communication: Ability to explain complex ML infrastructure concepts to business stakeholders and translate business requirements into structured technical scope for engineers; comfortable as connective tissue between teams in a fast-paced environment.

Nice-to-Haves

• Experience partnering with PhD-level AI/ML researchers.
• Background in real-time decisioning, predictive modeling, or building digital twins/context models.