Technical Program Manager, Enterprise

Key Responsibilities

• End-to-End Program Ownership: Own the strategic planning, scheduling, and high-velocity execution of multiple enterprise-grade programs, ensuring on-time delivery against aggressive product goals. Run weekly cross-functional syncs, surface blockers, drive decisions.
• Cross-Functional Architecture Integration: Manage complex dependencies and technical communication across core teams (e.g., Platform, Forward Deployed Engineering, Product) to seamlessly deliver frontier agents to our enterprise customers.
• Technical Translation & Executive Influence: Synthesize deep technical complexities into concise, actionable insights for both engineers and C-suite stakeholders. Drive absolute clarity across the delivery team regarding priorities, risks, and strategic outcomes.
• Risk & Dependency Mitigation: Proactively identify, track, and architect mitigations for technical risks unique to enterprise AI deployment, maintaining momentum in the face of ambiguity.
• Process Evolution: Modernize and scale agile execution frameworks (e.g., Jira, Linear) to support rapid, iterative machine learning and software development lifecycles.
• Metrics-Driven Accountability: Define, track, and report on key program health metrics, delivery forecasts, and engineering bottlenecks directly to executive leadership.

Minimum Qualifications

• 5+ Enterprise-Scale Experience: 5+ years of experience as a Technical Program Manager or in a technical leadership role managing complex, large-scale software engineering or machine learning development projects.
• Engineering Domain Expertise: 2+ years of dedicated experience managing programs focused directly on core engineering infrastructure, platform services, or distributed systems.
• AI/ML Literacy: Strong foundational understanding of the Generative AI lifecycle, including LLM utilization for structured downstream tasks, model fine-tuning, and performance evaluation.
• Masterful Communication: Proven track record of presenting to and influencing executive-level stakeholders, with the ability to translate complex technical challenges into clear business impacts.
• Execution Excellence: Advanced proficiency with iterative development methodologies and modern project management tooling (Jira, Linear, etc.).
• Growth Mindset: An insatiable appetite for learning and deeply engaging with modern ML/GenAI practices and infrastructure.

Nice-to-Have Qualifications

• Engineering Roots: Strong software engineering fundamentals, ideally with prior professional experience as a software engineer or data developer before transitioning into program management.
• Platform Adoption Track Record: Proven success driving the internal adoption of technical platforms, SDKs, or APIs across disparate product lines or independent business units.
• Data-Centric AI Familiarity: Direct experience working with data quality pipelines, LLM-as-a-judge evaluation frameworks, or automated RLHF systems.