[Hiring] Technical Product Manager - AI @hireVouch

Role Description

As a Senior Technical Product Manager, you will help translate scientific research, machine learning development, and cloud-based engineering into a clear, production-focused roadmap that leadership and external stakeholders can rely on for planning and execution. The work connects applied research, software delivery, and commercial readiness into a single, visible operating model.

As the company scales its platforms and enterprise-facing systems, you will establish the execution frameworks, dependency management practices, and communication layers that allow complex technical progress to be understood, sequenced, and delivered with confidence.

Key Responsibilities

• Execution framework ownership: Establish and maintain progress tracking systems and dashboards that provide leadership with clear visibility into development velocity and milestone delivery on a recurring basis.
• Requirements translation: Partner with business and commercial stakeholders to convert customer needs and market signals into well-defined technical requirements for engineering and data teams.
• Executive partnership: Serve as a technical advisor to senior leadership, communicating tradeoffs, risks, and timelines in a way that supports go-to-market planning and decision-making.
• Architecture alignment: Collaborate with engineering teams to ensure cloud-native systems and data platforms can support large-scale data processing and model deployment.
• Infrastructure strategy support: Contribute to the evolution of machine learning service infrastructure to meet performance, reliability, and scalability needs for enterprise-facing use cases.
• Dependency management: Coordinate the lifecycle of interconnected data pipelines, models, and services, ensuring upstream and downstream changes are communicated and integrated across teams.
• Sprint and ritual leadership: Lead day-to-day execution practices, ensuring work is well-scoped, documented, and tracked through consistent engineering workflows.
• Cross-team orchestration: Manage critical paths across data engineering, machine learning, and cloud infrastructure to prevent delivery blockers and maintain development momentum.
• Resource and priority sequencing: Support leadership in making informed decisions about technical investments, tradeoffs, and the ordering of major initiatives.

Tech Stack

• Data and ML systems: Large-scale data ingestion pipelines, machine learning pipelines, model deployment infrastructure.
• Program and delivery tools: Engineering ticketing systems, dashboarding and reporting tools, sprint and workflow management platforms.

Qualifications

• 5+ years owning product delivery in deeply technical domains (data platforms, ML products, infrastructure-heavy systems, or developer/platform products).
• Experience leading or managing programs involving large-scale data platforms, machine learning systems, and cloud infrastructure.
• Ability to translate complex technical and scientific work into clear, business-focused updates for senior stakeholders and leadership.
• Capability to operate in early-stage ambiguity while building the foundations enterprise customers expect around delivery, documentation, reporting, executive communications, and operational maturity.
• Proficiency in reasoning about system design, data flows, and ML lifecycle to ask sharp questions, spot gaps, and drive clarity.
• Experience translating ML work into product outcomes, including understanding datasets, model iteration cycles, evaluation metrics, monitoring, model drift, and the realities of deploying and operating ML in production.
• Comfort with cloud-native architectures and the tradeoffs involved in scaling ingestion, compute, storage, latency, reliability, and cost.
• Ability to create crisp requirements that reduce ambiguity (well-scoped epics, user stories, acceptance criteria) and definitions of done that teams can execute against.

It’s a Bonus If

• You have experience working with data collected from sensor-based, geospatial, or field-deployed systems.
• You have worked with (or adjacent to) atmospheric modeling, forecasting systems, or high-stakes environmental prediction.
• You have experience supporting government customers, utilities, or regulated enterprises, including longer buying cycles, compliance requirements, and high standards for documentation and auditability.
• You owned the implementation of reporting frameworks for board-level and executive-level technical visibility.