Product Manager, Agent Harness & Modelling
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 limits future possibilities. Own North's agentic evaluation framework, ensuring evaluations are compatible with both the North harness and Modeling's training infrastructure, serving as a reliable bridge between product and research. Engage enterprise customers to identify real-world agentic failures and translate findings into product and model requirements. Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that align North's architecture with emerging standards.
Insurance Product Manager
The Insurance Product Manager is responsible for owning the full lifecycle of AI extraction workflows on the platform, including scoping, architecture, prototyping, evaluation, and iteration. They will design and build complex insurance workflows such as submission intake, policy comparison, underwriting audits, and claims workflows into structured, testable AI workflows from scratch. The role includes defining ground truths and evaluation sets to measure accuracy and quality, running continuous benchmarks, and identifying quality gaps before customers do. They will work directly with customers and Forward Deployed Engineers to configure, test, and iterate workflows toward production, bringing insurance process expertise and technical judgment to every deployment. The Insurance Product Manager acts as the bridge between domain expertise and engineering teams by translating insurance needs into technical solutions.
Investment Summer Associate - AI Tooling
Design and build a proprietary AI-powered sourcing tool for the Investment Team; work cross-functionally with investors to understand sourcing workflows and pain points; attend founder events, hacker house demo days, accelerators, and technical meetups to identify emerging builders; conduct calls with founders and support active deal diligence; contribute research that informs ongoing investment thesis development; serve as a thoughtful and professional ambassador for M13 within technical communities; build a sourcing tool that meaningfully improves how the team identifies and evaluates opportunities; develop structured documentation for tool handoff and iteration.
Product Manager, Safety Research
Serve as the product bridge between Cohere's safety research teams and North, ensuring that findings from model evaluations, red-teaming, and behavioral research translate into product-level guardrails, controls, and safeguards. Own the safety product roadmap for Cohere and North, prioritizing features based on research findings, observed misuse patterns, evolving threat vectors, and customer requirements. Partner with modeling teams to scope and interpret safety evaluations, understanding how Cohere’s underlying models behave across adversarial inputs, edge cases, and high-stakes use cases. Define and drive evaluation frameworks for assessing how safety properties hold up as models and product capabilities evolve, ensuring regressions surface before they reach customers. Coordinate the development of guardrails and intervention mechanisms by working across research, engineering, and policy to determine where and how safety controls should be implemented within North's product layer. Monitor the AI safety research landscape and ensure North's roadmap reflects current research on prompt injection, jailbreaks, and emerging misuse patterns in agentic systems. Build processes for scaling safety review as North's surface area grows, including assessing safety risks of new features before launch.
Product Manager, Personalization
As a Product Manager for Memory & Personalization at OpenAI, you will define how ChatGPT learns from and adapts to individual users over time by working at the intersection of product, research, and engineering to design systems that capture meaningful signals from user interactions and translate them into personalized experiences. You will spearhead the development and implementation of AI features by crafting the vision, strategy, roadmap, and execution plan, convert user feedback into detailed product requirements, narratives, and technical specifications, utilize data to understand user needs and guide product development, and collaborate closely with research, product design, and engineering teams to bring new capabilities to life. This role also involves balancing product innovation with safeguards around user control, privacy, and transparency.
Senior Product Manager – Data & Quality
The Senior Product Manager – Data & Quality at Snorkel AI is responsible for partnering with frontier AI research labs to design datasets and environments that enhance model performance. They lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria. The role involves probing model behavior through systematic evaluation to identify weaknesses and high-impact data interventions, designing evaluation frameworks, calibration processes, and quality rubrics to establish measurable project success metrics. Additionally, they develop technical specifications for data projects balancing research rigor with operational feasibility, serve as a thought partner to customer research teams throughout the sales cycle to build trust and credibility, and stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies.
Head of Product, AI
Own the end-to-end AI product strategy grounded in technical feasibility and real-world constraints. Translate model capabilities, data limitations, and evaluation results into clear product decisions. Make hard trade-offs across quality, latency, cost, reliability, and user experience. Work daily with ML, backend, and mobile engineers on design, evaluation, and iteration. Define success metrics and feedback loops across offline evaluation, online experiments, and human feedback. Drive execution with clear specifications, risk awareness, and disciplined prioritization. Ensure AI features ship quickly, safely, and reliably into production. Own AI product quality across UX, correctness, and outcomes.
Head of Product, AI
Own the end-to-end AI product strategy, grounded in technical feasibility and real-world constraints. Translate model capabilities, data limitations, and evaluation results into clear product decisions. Make hard trade-offs across quality, latency, cost, reliability, and user experience. Work daily with ML, backend, and mobile engineers on design, evaluation, and iteration. Define success metrics and feedback loops across offline evaluation, online experiments, and human feedback. Drive execution with clear specifications, risk awareness, and disciplined prioritization. Ensure AI features ship quickly, safely, and reliably into production. Own AI product quality across UX, correctness, and outcomes.
Product Manager, Models
Own product strategy and roadmap for Heidi's models platform including evaluation, safety, model routing, and fine-tuning infrastructure, setting clear goals and being accountable to achieving them. Prioritise the team's work across enablement requests, model safety and quality, and new capability bets. Identify and resolve where product teams get stuck on models by fixing the platform. Build evaluation tooling and fine-tuning workflows usable by engineers and product teams in clinical settings. Decide improvements based on clinician feedback, model quality signals, and product team requests. Allocate engineering capacity among competing product teams and communicate deferrals clearly. Collaborate with engineers on evaluation design, fine-tuning decisions, and model architecture at a technical level. Set model quality and safety targets grounded in clinical outcomes. Consolidate infrastructure duplicated across product teams. Monitor foundation model developments and update the roadmap accordingly. Reporting into Product leadership, this platform role supports every user-facing product at Heidi.
Research Product Manager — Structured AI Systems
The Research Product Manager is responsible for advancing foundational research work related to tabular data learning and large tabular models, structured and relational representation learning, compression-aware AI, hybrid symbolic and neural systems, and the intersection of information theory, learning theory, and large-scale systems. They ensure research moves forward coherently and efficiently by connecting people, ideas, compute, and systems, facilitating the transition of research into durable capabilities. Responsibilities include productionization of structured AI models by working with research and systems teams to design training infrastructures for large tabular models, define inference architectures, and maintenance loops while understanding storage and compute trade-offs. They are also tasked with economic value extraction by identifying the buyers, economic value points, quantifying value, and converting research capabilities into revenue and platform advantage. Additionally, they identify viable research modeling advances, discontinue non-viable ones, define integration paths into enterprise workloads, and collaborate closely with the Chief Research Scientist on research agenda prioritization.
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