AI Product Manager, Open AgenticOpen Agentic is where we're building the next generation of AI products for insurance — products that can reshape how insurance is distributed, understood and managed. Through partnerships with brands including Avios and MoneySuperMarket, we already have AI products live in the market with household names.We're hiring two AI Product Managers to help us 10x the pace. You'll work directly with the Director of Product, Open Agentic, on some of the highest-profile products in the company, with significant autonomy and end-to-end ownership. You'll build to prove an idea with working prototypes, then work with our engineering team to take it into production.Think startup within a startup: high agency, high visibility, very little bureaucracy and lots of room to grow.What you'll doThis is a hands-on product role — as much about deciding what to build as building it. You'll work across product thinking, prototyping, engineering, AI, UX and deployment rather than owning one narrow part of the stack.Current Open Agentic product areas include:<ul><li>Global Product Graph, our structured intelligence layer for insurance products.</li><li>Companion, our consumer-facing AI agent. Today it guides customers through buying insurance. Before long, it should let them put their insurance on autopilot.</li><li>AI distribution, including ChatGPT Apps, MCP quote integrations and partner deployments.</li><li>Agentic Safety Platform, which helps companies evaluate, monitor and manage customer-facing AI deployments.</li></ul>We don't expect these to be the only things you'll work on. Part of the role is helping us discover and build what comes next.You'll:<ul><li>Take ambiguous product ideas and turn them into working software.</li><li>Decide what to build now, what to defer and what not to build at all.</li><li>Ship quickly, get things in front of real users and iterate on what you learn.</li><li>Own products beyond the prototype — from zero to one, then from one to 100.</li><li>Talk to customers and partners to find the problem worth solving, not just to validate a solution.</li><li>Own the Global Product Graph — the ontology, the data, and the workflows that keep it accurate.</li><li>Develop the insurance intelligence on top of it: product scoring, compliance evaluations, and what "good" means for a regulated AI deployment.</li><li>Help shape both what Open Agentic builds and how we build it.</li></ul>Within your first three months, we'd like you to have taken meaningful ownership of an AI-native product and shipped it. Within six months, to be owning a major product or workstream end-to-end and taking it towards real customer adoption and revenue.Who we're looking forWe're looking for builders — people who get excited by a blank page, move quickly and naturally take ownership when something needs doing.You'll probably have an engineering or computer science background and be able to build things yourself, but your ambitions sit in product: more interested in whether we're building the right thing than in owning the system that runs it.You're probably more excited by the product and commercial applications than by perfecting the engineering, and you want the next few years to grow your product and commercial mind rather than your engineering craft.You can build. You should be able to show us something you've personally built and deployed — we don't care whether it has 1 user (you) or 10 million — and explain how it works and the trade-offs you accepted. We're not asking you to own production systems. We are asking that you can prove an idea without needing a team.You have product judgment. You start from the problem and who has it, not the feature. You're opinionated about what to build, comfortable arguing for what to leave out, and you can say how you'd know whether it worked. You notice when something feels bad — good UX, design and clear communication matter to you alongside good engineering — and you care whether people actually use the thing, not only whether the You're AI-native. AI isn't something you recently added to your workflow because everyone else is talking about it — it's already fundamentally changed the way you build. You're deeply comfortable with tools such as Claude Code, the latest coding models, agentic development workflows and platforms such as Vercel, and you have opinions about which are good at what. You've also built with models rather than just alongside them, and know where they're reliable and where they aren't. You keep up with new models and research because you find the space genuinely exciting, and you have a view on where it's going.You have high agency. You don't need a perfectly written PRD, detailed Jira tickets and three meetings before you can start. You're comfortable with: "Here's the problem. Go figure out what we should build."You're hungry to grow. You'll probably have 2+ years building products, but we're far more interested in what you've done than how long you've worked or what credentials you have.Nice to haveNone of this is required, but any of it is useful: insurance, financial services or another regulated industry; shipping AI or ML products in production; evaluations, model judging, red-teaming or guardrails; knowledge graphs, structured extraction or retrieval; Python, TypeScript and modern cloud infrastructure; or fast-moving startup experience.Why join?We have AI products live with household-name brands, real customers using them and the regulatory groundwork in place to deploy AI safely in insurance. What we want next is pace — more products, shipped faster, on foundations that already exist.You'll get:<ul><li>Real ownership: take products that matter to Open's future from an idea to something customers pay for.</li><li>Direct access to leadership: work with the Director of Product and closely with senior leadership, on work seen across the company and by major external partners.</li><li>Speed: a small team where good ideas become live products quickly, using the best available AI tools rather than legacy ways of working.</li><li>Room to grow: as Open Agentic scales, your scope should scale with it.</li><li>Competitive compensation, including share options, so everyone shares in our success.</li><li>Flexible working, paid company parental leave and a personal development allowance.</li></ul>If you want a narrowly defined role where somebody else decides what gets built, this probably isn't the right job.If you want to build things from scratch, ship constantly and have an outsized impact on an AI business that's already live and about to get a lot bigger, we'd like to meet you.Role locationBased in our Marylebone office in London, hybrid, with at least three days a week in the office, a rhythm that supports collaboration and the fast, high-trust culture we're building.You'll work with colleagues across the UK and Australia, including our broader Product and Engineering teams.You'll need the right to work in the United Kingdom.How to applySend us your CV, but don't write a cover letter. Instead, answer three short questions:<ol><li>Send us a link to something you've built and deployed recently — a side project, website, tool or something professional. In 20 words or fewer, what is it and what did you personally do?</li><li>What's your current preferred coding model / AI development setup? Tell us why in 20 words or fewer.</li><li>One AI product or tool that you love — not ChatGPT or Claude. Why, in 20 words or fewer.</li></ol>Don't overthink the answers. We're just looking for genuine builders with curiosity and opinions.Interview processWe want the process to be fast, practical and representative of the actual job. There's no algorithm trivia and no whiteboard coding test.<ol><li>Initial conversation with the Director of Product, Open Agentic — what you've built, how you work and what you're looking for next.</li><li>A take-home build. We'll give you a real Open Agentic product problem and ask you to build and deploy something, using Claude Code or whatever tools you like. Then you'll walk us through it with a small group from product and engineering.</li><li>Final conversation with Open's co-CEOs, focused on ambition, culture and where you want to grow.</li></ol>We want to move quickly when we find exceptional people.More about usInsurance is one of the world's least digitally mature industries: confusing, paper-based and heavily intermediated. We launched in Australia in 2016 to digitise the entire process, opened our London office in 2022 and our Manchester office this year. We're a Certified B Corporation with a team across the UK, Australia and New Zealand.We do this through three products. Open Embedded lets businesses offer insurance directly inside their own apps and websites. Open Agentic uses AI safely and responsibly to guide customers through buying a policy, answering questions in plain English. Open Intel spots the moments when someone is most likely to need cover — like buying a car or moving house — and helps us reach them at the right time.Diverse perspectives and lived experiences make us better at what we do. If your CV doesn't tick every box, apply anyway. Let us know if you need any adjustments through the process and we'll make it work.Open is for builders — people who are intellectually curious, courageous and reject the idea that "it can't be done". If that sounds like you, apply and let's build the future of insurance together.

AI Product Manager

Job OverviewAt Relativity, we build platforms that help our customers find the truth in complex data and act on it with confidence. We are hiring a Senior Product Manager to join our Automation Services and Infrastructure organization, overseeing the teams responsible for the platform capabilities that power RelativityOne. This role spans automation, critical security primitives, cloud-native infrastructure, and AI-powered extensibility: the governed operational foundation that reliably powers the agents, tools, and experiences delivering Legal Data Intelligence. You will define product direction, establish clear product health and success indicators, and partner closely with engineering and cross-functional stakeholders to deliver resilient, secure, and scalable platform solutions. A successful candidate brings a strong technical foundation, a customer-centric mindset, and exceptional communication skills, intrinsic adoption of AI into workflow, along with proven experience leading through influence and collaborating across product, engineering, and partner teams. If you enjoy building for builders, we'd love to talk!Job Description and RequirementsRole Responsibilities:Own it: strong product execution across the lifecycle <ul><li>Own the roadmap and backlog. Sequence work based on customer impact, technical dependencies, and business priorities. </li></ul><ul><li>Define what "done" looks like and hold to it. Regardless of whether the work is an early incubation, a platform capability driving active migration, or a mature product managing adoption at scale. The phase changes; the ownership standard does not. </li></ul><ul><li>Get it in front of real customers or internal consumers early and often. Run structured discovery with developers, engineering teams, and ISVs to surface what is not working, validate what is, and generate the evidence that drives the roadmap. </li></ul><ul><li>Make hard scope calls. Own the triage of what ships this quarter, what is a future bet, and what is out. Make those calls transparently, document the reasoning, and keep momentum. </li></ul><ul><li>Drive velocity while holding the quality bar. Your customers - and the engineering teams that depend on your platform - need to defend their work downstream. Platform bugs compound. Hold the shipping cadence and the reliability standard together. </li></ul><ul><li>Know when you are managing discovery versus managing adoption. Launching a net-new capability requires different instincts than scaling an existing one from 10% to 50% of customers. Apply the right model for where the product is, not where you wish it were. </li></ul>Build the right thing: platform-first product thinking <ul><li>Define what the capability means for the developers and end users who depend on it. Platform PMs own things others build on - a wrong decision in versioning, access control, or API design compounds across dozens of teams. Build for long-term stability, not just the next release. </li></ul><ul><li>Design for the human-in-the-loop. Whether in AI-driven workflows, permission management, or automation pipelines - the right escalation path, governance signal, and audit trail make operators more capable. Build for that model deliberately. </li></ul><ul><li>Treat trust as a product foundation. Security, auditability, and platform governance are the moat. Partner with engineering and security to keep defensibility a first-class design constraint from day one. </li></ul><ul><li>Use AI as a force multiplier. Use AI capabilities - in prototyping, research synthesis, spec drafting, and customer insight - to move faster. Model this for the team and share your innovations with the rest of the organizations because we'er all learning and in this together! </li></ul>Contribute to the right ecosystem <ul><li>Define the platform patterns that scale. Working with engineering and architecture, apply the contracts and governance boundaries that let internal and external builders develop on a stable, well-governed surface. Versioning, security, capability scoping, and backward compatibility are product decisions that compound. </li></ul><ul><li>Translate the platform to its builders. Turn technical concepts - cloud-native migration patterns, API security models, AI extensibility standards - into narratives that resonate with developers, domain experts, and buyers. </li></ul><ul><li>Develop customer-facing documentation, enablement guides, and release notes for platform capabilities. Capture best practices from early adopters and package them into reusable assets that accelerate adoption. </li></ul>Lead through influence: cross-functional and data-driven <ul><li>Be the connective tissue across product, engineering, and go-to-market for your area. Hold the picture across the development experience, runtime governance, distribution, and customer value. Pull the right people together, keep them aligned, and clear the blockers. </li></ul><ul><li>Track the right metrics. Adoption, migration progress, platform reliability, and time to first successful integration. Define the leading indicators before the lagging ones catch up. </li></ul><ul><li>Communicate progress and risks clearly. Report what you know, what you do not, and what you need - to your leadership, your engineering partners, and your customers. </li></ul> Minimum qualifications <ul><li>7+ years in software product management, with demonstrated ownership across more than one product lifecycle stage - incubation, active migration, or scaled adoption. </li></ul><ul><li>Demonstrated ability to deliver through ambiguity: you have defined scope under uncertainty, made hard prioritization calls, and shipped with customers in hand. </li></ul><ul><li>Experience with developer-facing or platform-facing products - APIs, SDKs, extension frameworks, security primitives, or cloud infrastructure - with measurable adoption. </li></ul><ul><li>Hands-on experience with cloud platform architecture (Azure preferred) and comfort in technical discussions around distributed systems, platform APIs, and cloud-native design. Working knowledge of AI-enabled platform patterns - developer tooling, agentic workflows, or AI-native services - and where reliability and governance constraints bind. </li></ul><ul><li>Solid understanding of the software development lifecycle and modern delivery practices, including experience in AI-powered SDLC environments where agentic tooling is part of how the work gets done. </li></ul><ul><li>Data-driven: you use data to set goals, size bets, monitor product health, and change course. </li></ul><ul><li>Strong communicator across technical and non-technical audiences. You translate complex platform and infrastructure concepts into clear customer and business outcomes. </li></ul><ul><li>Active user of AI tools to prototype, synthesize research, draft specs, and move faster. This is how the work gets done here. </li></ul><ul><li>Comfortable working alongside or mentoring junior product managers - you've contributed to someone's growth as a PM, even without a formal reporting relationship. </li></ul> Preferred qualifications <ul><li>Background in legal technology, eDiscovery, compliance, or another domain defined by complex, high-stakes workflows where defensibility, reproducibility, and audit trails are first-class requirements. </li></ul><ul><li>Experience with cloud-native platform migrations: moving workloads off legacy frameworks onto modern compute, storage, and auth primitives. </li></ul><ul><li>Familiarity with emerging AI integration standards such as Model Context Protocol (MCP) or Agent-to-Agent (A2A) and how they shape platform extensibility. </li></ul><ul><li>Experience building on or contributing to developer ecosystems, partner integrations, or extensibility platforms at enterprise SaaS scale. </li></ul><ul><li>A technical foundation (computer science, engineering, data science, or equivalent) that lets you work credibly with senior engineers. </li></ul>Relativity is committed to competitive, fair, and equitable compensation practices.This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.The expected salary range for this role is between following values:$140,000 and $210,000The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position. Required Skills:Agile Methodology, Innovation, Leadership, Market Research, Market Strategy, Product Development, Product Management, Roadmapping, Team Leadership, User Experience (UX)

Senior Product Manager - Platform & Automation

Job OverviewRelativity is a leading legal data intelligence company building AI technology that helps organizations organize data, discover the truth, and act on it with confidence. Over two decades, the company has built the most trusted platform in legal data, earning deep relationships with the world's leading law firms, corporations, and government agencies, and managing petabytes of the most sensitive data in existence. That foundation is now being turned into something larger: The AI platform for legal data intelligence. The Relativity Intelligence Model is the architecture for that transformation. At its base is the Foundational Layer (Relativity's shared data platform serving all AI applications). Five primitives give AI agents the structure, meaning, and retrieval capability they need to reason over legal data at scale: Files, Ontology, Data Capabilities, Knowledge/Metadata, and Query Plane. Relativity's Data Intelligence Foundation engineering org is building this layer. The product leadership that shapes it, drives adoption across Relativity's product teams, and builds the PM discipline to own it long-term. That's this role. The Director of Product Management, Data Intelligence Foundation is one of the highest-leverage product roles at Relativity. The Foundational Layer is what makes every Relativity aiR application smarter, every agent more reliable, and every Relativity product team faster. Getting it right matters enormously.Job Description and RequirementsWhat you'll own The full PM layer across multiple engineering orgs: <ul><li>Files / Natives: The storage primitive for legal documents, images, and native files. You define the substrate that makes immutable legal data consistently accessible across every product, partner integration, and AI workflow, with the SLAs, access contracts, and API surface that teams can build on with confidence. The underlying data primitives are the foundation; the degree to which the retrieval layer (Query Plane) matches this structure determines how easily the organization can navigate between "slow data" and "fast data" use cases. </li></ul><ul><li>Ontology / Relationship: The semantic layer of the Relativity Intelligence Model. Ontology encodes meaning: what kinds of things exist in legal data and how they relate, so that AI agents can reason, not just query. You define what Relativity's Ontology becomes: the entities, relationships, and contracts that give every Skill and Agent a shared vocabulary for legal data. </li></ul><ul><li>Data Capabilities: Reporting, Audit, and internal data infrastructure. The operational backbone that makes the platform observable, auditable, and explainable. These are non-negotiable properties in legal data intelligence use cases. </li></ul><ul><li>Knowledge / Metadata: The core data model that every product team, customer, and integration partner works with. A unified materialized document layer, consistent across all workspaces, is the mandate. Your roadmap evolves this surface to serve AI application teams as first-class consumers alongside the users who have relied on it for years. </li></ul><ul><li>Query Plane: One of the most performance-sensitive and strategically important services in the product. The mandate is a unified retrieval pillar with a rich materialized document layer: standardized ingestion APIs independent of data source, hybrid retrieval (lexical + vector) with reranking, chunking as a managed capability, and tiered storage (cold/warm/hot). </li></ul>Minimum qualifications <ul><li>12+ years in product management; 5+ years leading platform or infrastructure PM organizations </li></ul><ul><li>Deep fluency with data platform primitives, including storage systems, metadata layers, knowledge graphs, query engines, or equivalent. You can design an API contract, contribute to engineering scope decisions, and articulate the trade-offs in a data consistency model. </li></ul><ul><li>Demonstrated ability to manage and build a PM team through hiring, leveling, and establishing product practice for a new domain </li></ul><ul><li>Bachelor's degree in Business, Computer Science, Engineering, or Design, or comparable work experience </li></ul>Preferred qualifications <ul><li>Experience building at companies where the data platform is the product, not a supporting system. Snowflake, Databricks, Elastic, MongoDB, Palantir, and similar are strong indicators of the right background. </li></ul><ul><li>Experience building for AI systems, agents, or ML pipelines as primary consumers. You understand what a model needs from data that a human doesn't, and you design for both. </li></ul>Ways of working <ul><li>Engineering credibility is paramount. You will be in technical discussions with skilled and knowledgeable engineering leaders regularly. You need to be a peer in those conversations, not a relay. </li></ul><ul><li>Organizational cadence. You establish the operating rhythm for a multi-pillar PM org: the forum structure, planning cadence, and decision frameworks that let the team move with velocity and alignment. When priorities conflict across pillars, you hold the trade-off clearly and resolve it cleanly. </li></ul><ul><li>Internal GTM ownership. Adoption of the Foundational Layer means every product team at Relativity builds with it. You define how internal teams discover, integrate, and get value from platform services, and you measure it. This is a product strategy job and a change management job simultaneously. </li></ul><ul><li>Coaching a scaling PM org. You build PM capability, not just PM headcount, leveling people up while running at speed. </li></ul><ul><li>Cross-functional influence without authority. You don't control the teams that need to adopt what you build. You make the new path clearly better and bring teams along through clarity, evidence, and trust. </li></ul><ul><li>Directional clarity under ambiguity. Several of these primitives are being defined as the engineering teams build. You make good decisions with incomplete information and update them when required. </li></ul><ul><li>Legal domain expertise is not required. However, internalizing why defensibility, chain of custody, and auditability are first-class design requirements. </li></ul>Relativity is committed to competitive, fair, and equitable compensation practices.This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.The expected salary range for this role is between following values:$188,000 and $282,000The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.

Director, Product Management - Data Intelligence Foundation

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