Staff Technical Program Manager, Machine Learning

Overview

As Technical Program Manager for Machine Learning Infrastructure, you will oversee the portfolio spanning inference, serving, and endpoints to scale Cohere’s AI infrastructure for a growing user base. You’ll coordinate cross-functionally with Modeling and customer-facing teams, identify pain points, and implement processes that boost development velocity while meeting user needs. You’ll foster a culture of continuous improvement, drive prioritized delivery, and partner with senior leaders to solve strategic problems that impact the company. This role offers exposure to cutting-edge AI tech and a dynamic, fast-paced environment with meaningful impact.

Pay / Benefits

• weekly lunch stipend
• health and dental benefits
• RRSP matching, 401K, Pension
• 6 weeks of paid vacation
• parential leave top-up (up to 6 months)
• home office stipend

Responsibilities

• Manage the ML infrastructure program portfolio (inference, serving, efficiency, endpoints) to scale for internal and external users
• Lead end-to-end program coordination with cross-functional partners (Modeling, customer-facing teams)
• Identify bottlenecks and establish processes that enable development while serving user needs
• Promote continuous improvement and incident management practices with timely root-cause analysis and fixes
• Prioritize overlapping projects to align with top company priorities
• Collaborate with stakeholders to set timelines, deliverables, budgets, and scope
• Deliver clear updates to engineering, leadership, and non-technical audiences
• Act as a tactical and strategic partner to senior program leads, addressing both execution and high-level strategy

Key requirements

• 5+ years of Technical Program Management experience in Machine Learning Infrastructure (inference, serving, efficiency, endpoints)
• Deep technical knowledge of ML infrastructure design and engineering best practices
• Comfort in fast-paced, low-structure environments and willingness to do hands-on work if needed
• Experience advocating for infrastructure and modeling teams to build scalable, high-performance ML systems

• pragmatic and execution-oriented
• organized and cross-functional collaboration
• adaptable in fast-changing environments
• ML infrastructure design and implementation
• model inference and serving
• system efficiency improvements

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