Product Manager, Agent Harness & Modelling

We are seeking an Agent Harness Product Manager to own the execution layer that makes North agents reliable, capable, and production-ready. This is a role that sits at the intersection of three domains: Agent Loop and Execution: Own the core agent runtime: tool orchestration, parallel execution, sub-agent delegation, sandbox code execution, and failure recovery. You will define how North agents plan and act across long, multi-step workflows and ensure the execution environment is robust enough for the most demanding enterprise tasks. You are expected to engage at the implementation level, contributing to architecture decisions alongside engineering rather than simply handing off requirements. Context Engineering: Own how our Agents manage the context window as a deliberately controlled resource. This includes progressive disclosure of tools and skills, context compaction and summarization, offloading of large payloads to a persistent filesystem, and the instrumentation that keeps agents oriented across extended trajectories. Model-Scaffolding Co-evolution: Own the feedback loop between North's harness and the Modeling Team. This PM is the connective tissue that makes that possible: ensuring harness design decisions are validated by Modeling before they are built, that evals are the shared bridge between both teams, and that as the harness evolves the model evolves with it.

Responsibilities

Define and own the roadmap for North's agent harness, including the agent loop, context engineering layer, tool orchestration, sandbox execution, and sub-agent delegation; Serve as the primary interface between North engineering and Cohere's Modeling team, ensuring new harness capabilities are validated before being built and that neither team paints itself into a corner; Own North's agentic evaluation framework, ensuring evals are compatible with both the North harness and Modeling's training infrastructure, and that they serve as a reliable bridge between product and research; Engage enterprise customers to surface real-world agentic failures and translate findings into concrete product and model requirements; Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that keep North's architecture aligned with emerging standards.

Requirements

5+ years of product management experience in agentic AI systems, developer infrastructure, or applied ML products; Deep understanding of modern LLM agent architectures, including multi-agent systems, tool-augmented reasoning, memory and retrieval, programmatic orchestration, RAG, and long-horizon execution; Strong grasp of agentic evaluation design, including how to measure task completion, failure recovery, and long-horizon reliability, and how to diagnose model vs. scaffolding gaps; Technically deep enough to contribute to architecture decisions at the implementation level: comfortable reviewing and shaping design docs, reasoning about async execution patterns, sandboxed environments, filesystem design, and the tradeoffs that come with building harness capabilities into a production platform; Ability to flex between ML research conversations and engineering architecture discussions with equal fluency; Track record of shipping platform-layer products with demonstrated impact on reliability, performance, or capability.

Nice to have

An active practitioner of agent frameworks who regularly builds with and follows the latest developments in open-source harnesses, coding agents, and orchestration tools in both professional and personal work; Hands-on experience with enterprise agentic deployments: multi-tenant orchestration, tool permissioning, audit trails, and compliance requirements; Familiarity with infrastructure constraints relevant to enterprise deployments: on-premises environments, scalability challenges, and the operational tradeoffs of running complex agent workloads in restricted or air-gapped settings; Prior work at the intersection of research and product, translating nascent model capabilities into shipped product features; Background working within or closely alongside an ML research or post-training team

Benefits

Impact: Shape how Canada's most important public institutions adopt and deploy frontier AI; Innovation: Work alongside leading researchers and engineers solving complex ML challenges; Growth: Competitive compensation, equity options, and opportunities for professional development; Flexibility: Hybrid work model with offices in key global locations; Weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch; Full health and dental benefits, including a separate budget for mental health; RRSP matching, 401K, Pension Scheme; 100% Parental Leave top-up for up to 6 months for either parent; Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and workspace improvement credit; Education & learning stipend for conferences, courses, and coaching; 6 weeks of paid vacation (30 working days); Budget for traveling to other offices if you are remote, plus an annual company offsite; Daily lunch program, snacks, and community and social events for office employees; Co-working benefit for those not near an office; $500 home office stipend