Staff Product Manager (Data/ML Platform)

Location: Hybrid in New York or San Francisco; the company is fully remote, and the role may require up to 25% travel.

Base salary range: $194,395–$265,000 annually, depending on location, qualifications, experience, and skills. Full-time roles are eligible for equity in the form of stock options.

Company

hirify.global is a cybersecurity product company developing NodeZero, a platform for production-safe autonomous penetration testing and security assessment operations across internal, external, cloud, and hybrid cloud environments.

What you will do

• Define and execute the vision and multi-quarter roadmap for NodeZero’s Agentic Data Platform, including DBaaS, data services, APIs, and machine learning infrastructure.
• Lead managed relational, graph, and vector database capabilities, including provisioning, scaling, migrations, upgrades, lifecycle management, reliability, and performance.
• Own the unified asset and attack graph, data architecture, query APIs, change-data-capture pipelines, event streams, schema governance, and platform abstractions.
• Define machine learning platform capabilities such as training pipelines, feature stores, experiment tracking, model registries, inference, deployment, monitoring, evaluation, and training-data quality.
• Drive reliability, security, governance, tenant isolation, access controls, encryption, retention, deletion, disaster recovery, observability, and operational resilience.
• Partner with Engineering, ML Engineering, Design, Finance, Sales, Marketing, Customer Success, and executive leadership on investment decisions, adoption, launches, metrics, customer value, and commercial outcomes.

Requirements

• 8+ years of Product Management experience, including 4+ years building infrastructure, data, or machine learning platform products in B2B SaaS environments.
• Deep technical understanding of production databases, including relational, graph, or similar systems, with knowledge of replication, high availability, disaster recovery, indexing, and performance trade-offs.
• Experience building or managing scalable data platforms, including data pipelines, data modeling, schema evolution, data contracts, APIs, and distributed data architectures.
• Strong understanding of traditional machine learning infrastructure, including training-data pipelines, feature stores, experiment tracking, model registries, batch and real-time inference, and production model monitoring.
• Experience defining data governance and security requirements, including lineage, anonymization, retention, deletion, tenant isolation, and sensitive-data handling in multi-tenant SaaS environments.
• Financial acumen covering infrastructure cost models, total cost of ownership, ROI, investment business cases, and cloud infrastructure spending with Finance.
• Experience driving internal-platform adoption, establishing shared services, enabling engineering teams, and migrating users from existing solutions.
• Strong product judgment, business orientation, communication, stakeholder management, executive influence, and ability to translate technical concepts into business outcomes.
• Ability to operate strategically and tactically across platform vision, business strategy, PRDs, technical requirements, architecture reviews, prioritization, and launch execution; experience defining metrics, working in Agile environments, and leading cross-functional initiatives.
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.

Nice to have

• Previous experience as a database engineer, data engineer, ML engineer, Site Reliability Engineer, or technical founder.
• Experience with security data platforms such as XDR, SIEM, exposure management, or offensive security products.
• Hands-on familiarity with graph data modeling, entity resolution, vector databases, semantic search, labeling pipelines, ground-truth datasets, or training-data infrastructure.
• Experience with FinOps practices, cloud infrastructure optimization, or FinOps certification.
• Familiarity with MLOps technologies such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or Feast.
• Understanding of generative AI data requirements, including embeddings, vector search, and fine-tuning datasets.
• An advanced degree in engineering, computer science, machine learning, or business.

Culture & Benefits

• Remote-first company with an inclusive, collaborative culture focused on respect, ownership, learning, and results.
• Health, vision, and dental insurance for employees and families.
• Flexible vacation policy and generous parental leave.
• Career development opportunities in a fast-growing cybersecurity organization.
• Competitive compensation and equity for full-time employees.