Technical Product Manager, AI
Lead a cross-functional team including engineers, designers, data scientists, and researchers to develop generative AI-enabled solutions for external riders and internal operations. Drive discovery into unmet needs, shape product vision, define priorities to achieve customer and business objectives, establish success metrics, and explore technical feasibility. Work closely with leadership across Product & Experience, Software, and Vehicle Engineering to implement AI solutions for the ride-hail service. Design AI-generated capabilities to enhance consumer experience, utilize data and market insights to guide product strategies, integrate user research into product requirements, oversee planning and management of tools and product scalability, collaborate with engineers and designers, coordinate cross-functional teams to meet milestones, lead the creation and launch of generative AI products, and develop and analyze performance metrics to gauge product success.
Senior AI Product Manager
The Senior AI Product Manager at OpusClip is responsible for bridging the gap between complex AI research and seamless user experiences by transforming raw model capabilities and complex workflows into polished products that serve millions of creators. They act as the final filter for aesthetic quality, ensuring every feature meets high standards for rhythm, composition, and visual harmony. They lead rapid prototyping by building functional proofs-of-concept, working directly with APIs and codebase to validate hypotheses before full-scale engineering. Additionally, they identify latent creator needs and competitive gaps to prioritize bold and high-impact product bets over incremental iterations, architecting the future of digital storytelling through multimodal AI.
Senior AI Product Manager
The Senior AI Product Manager at OpusClip is responsible for bridging the gap between complex AI research and seamless user experiences by transforming raw model capabilities and complex workflows into polished products. They act as the final filter for aesthetic quality, ensuring every feature meets high standards of rhythm, composition, and visual harmony. They lead rapid prototyping by building functional proofs-of-concept, working directly with APIs and codebase to validate hypotheses before full-scale engineering. They also identify latent creator needs and competitive gaps early on and prioritize bold, high-impact product decisions over incremental changes, architecting the future of digital storytelling and video creation.
Principal Product Manager – Agentic AI Systems
Define and execute product initiatives for agentic AI systems focusing on measurable customer and business outcomes. Own significant parts of the agentic system lifecycle including orchestration, decisioning, evaluation, and iteration. Contribute to building a repeatable framework for launching, evaluating, and improving agentic capabilities across customers. Help define how agentic systems are measured and improved in production balancing autonomy with safety and reliability. Partner closely with Engineering, Applied AI/ML, Design, and Solutions teams to ship production-ready systems. Work directly with customers to understand workflows, requirements, and success criteria. Drive customer-informed prioritization by staying close to live deployments and real usage patterns. Support best practices for agent evaluation, iteration, and safe rollout. Represent the product in customer conversations, demos, and feedback sessions.
Senior Product Manager, Data & Retrieval
Drive the roadmap and strategy for Harvey's Data Factory to scale data 100x by building the legal index of the world. Work with internal operations and external data providers to expand coverage, accelerate execution, and improve dataset quality. Own and evolve the end-to-end data architecture, including ingestion, transformation, storage, indexing, and retrieval, ensuring performance, reliability, and scalability for LLM-powered products. Partner with Applied AI engineers to build and optimize retrieval systems, embeddings, search models, and evaluation frameworks. Architect and oversee large-scale ingestion pipelines that aggregate, normalize, and continuously update millions of heterogeneous legal documents across global jurisdictions. Collaborate cross-functionally with Product Engineering, Applied AI, Research, and Platform teams to deliver high-quality production systems supporting reasoning, summarization, and legal research workflows.
Product Manager, API Model Behavior
As a Model Behavior Product Manager for the API team, you will define strategic priorities and roadmap for improving model behavior for API users with a focus on user outcomes, safety, reliability, and emerging capabilities. You will partner with research and engineering teams at a technical level to translate those goals into model capability improvements. Additionally, you will collaborate with cross-functional teams to launch OpenAI's frontier models in the API, exposing their capabilities to users via flexible and powerful API primitives. You will develop scalable methodologies, tools, and processes for evaluating, tuning, and iterating on model behavior. Moreover, you will synthesize user research, community feedback, and quantitative insights to target improvements in AI models and establish and iterate on clear, actionable metrics that reflect model quality and user experience at scale.
Product Marketing Manager, Public Sector
The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, and building necessary primitives, data, and tooling. The candidate will partner deeply with core customers and frontier research labs to address complex technical problems related to model improvement, performance, and deployment. They are expected to shape and propose model improvement work by translating customer and research objectives into clear proposals and execution plans. Responsibilities include leading the end-to-end lifecycle from discovery through shipping initial solutions and scaling pilots, independently managing technical working sessions with senior stakeholders, defining success metrics, surfacing risks, and driving programs to measurable outcomes. The role requires cross-functional collaboration with research, platform, operations, security, and finance teams to deliver production-grade results. Additionally, the candidate will build robust evaluation frameworks, close the loop with data quality and feedback, and share learnings to enhance execution across accounts.
Forward Deployed Product Manager
The AI Outcomes Manager partners with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes using Glean. They lead strategic reviews and advise customers on their AI roadmap to maximize value from Glean’s platform. Responsibilities include translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They conduct discovery workshops, scope pilots, guide rollouts, and drive adoption of the Glean platform. The role also involves designing and building AI agents with customers, including rethinking and redesigning underlying business processes to maximize impact and usability, as well as proactively identifying expansion opportunities and driving engagement across teams and functions.
Product Manager
The role involves translating AI research into product solutions by working closely with client-side researchers on post-training, evaluations, and safety/alignment, and building the necessary primitives, data, and tooling. The candidate will partner deeply with core customers and frontier AI labs to tackle technical problems related to model improvement and deployment. Responsibilities include shaping and proposing model improvement work by translating customer and research objectives into clear, technically rigorous execution plans and statements of work. The role also requires leading the end-to-end lifecycle from discovery, writing product requirement documents (PRDs) and technical specifications, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling pilots into repeatable offerings. The candidate will independently run high-stakes engagements with senior stakeholders, define success metrics, surface risks, and drive programs to measurable outcomes. Collaboration across research, platform, operations, security, and finance teams is essential to deliver reliable, production-grade results. Additionally, the role includes building robust evaluation frameworks, closing the loop with data quality and feedback, and sharing learnings that enhance technical execution across accounts.
Business Operations & Strategy Manager
The role involves partnering closely with ML teams and AI research teams to scope, pitch, and translate frontier AI research needs into clear product roadmaps and measurable outcomes. The candidate will drive end-to-end delivery by collaborating with research teams and core customers to scope, pilot, and iterate on frontier model improvements while coordinating with engineering, operations, and finance to deploy high-impact solutions. Responsibilities include translating research into product by working with researchers on post-training, evaluations, safety, and alignment and building necessary primitives, data, and tooling. The position requires partnering with frontier labs and AI teams to solve technical problems related to model improvement, performance, and deployment. The manager must shape and propose model improvement work by translating objectives into technically rigorous proposals, manage the end-to-end lifecycle by leading discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, and scaling pilots. Leading complex, high-stakes engagements by conducting technical working sessions with senior stakeholders, defining success metrics, surfacing risks, and driving programs to outcomes is expected. Collaboration across research, platform, operations, security, and finance teams to deliver reliable production-grade results is required. The candidate will build evaluation rigor at the frontier by designing evaluation frameworks, closing the data quality feedback loop, and sharing learnings to enhance technical execution across accounts.
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