We're looking for a AI Product Builder to help define the next chapter of the browser - someone who ships their own prototypes, holds strong opinions on craft, and doesn't wait to be told where to start. You'll own a product area end-to-end: frame the opportunity, build the prototype that sells it, ship the experience alongside design and engineering, and measure how it lands.
Your work will shape how people move through the web every day.
What You'll Do:
Own product areas end-to-end, from discovery through post-launch iteration.
Turn ambiguous opportunities into opinionated proposals, backed by research, data and working prototypes.
Prototype the experience yourself - in Cursor, Claude, Figma, or whatever gets an idea into something people can feel.
Partner with and ship alongside design and engineering, from kickoff to polish.
Monitor performance post-launch and make the call on what comes next.

A security company, is building an internal AI capability across the organization: agents that perform real tasks as the organization, with ROI and security assessed at every step. We are looking for a senior engineer who owns this end to end - a hands-on builder who also carries the senior function: setting the technical strategy, making the architecture calls, and being the person leadership trusts on internal AI.
This is not a pure IC build role and not a pure strategy role. You will personally build the flagship systems - production agents, the context layer, the evaluation and safety stack - while also deciding what gets built, in what order, and to what standard. You will work across R&D, IT, Security, BizOps, Customer, Sales, Marketing, People, and Finance, with the AI Transformation Officer owning the business portfolio.
What you'll do - strategy and senior function:
Own the technical strategy for our internal AI transformation: target architecture, model and vendor choices, build-vs-buy calls.
Shape the AI portfolio: which workflows to automate, in what sequence, with what success metrics - and push back when the wrong thing is being built.
Set and enforce the bar on ROI and security for every AI system: cost, adoption, cycle-time impact, access control, and auditability are reviewed per project, not after launch.
Act as the senior technical voice for internal AI: advise leadership, review AI initiatives across the org.
Turn one-off builds into a real internal platform: reusable patterns, shared services, and standards that AI Champions and teams adopt.
Mentor AI Champions; raise the org's practical AI level.
Manage our AI- Champions Community.
Continuously evaluate models, vendors, and frameworks, and keep us agile.
What you'll do - hands-on build:
Personally design, build, deploy, and own the flagship internal agents and AI workflows, from prototype through production.
Build and evolve our AI context layer: connectors, APIs, retrieval services, metadata, identity and authorization, controls, and tool interfaces.
Create safe action tools for agents and infrastructure for agents to work.
Build RAG, tool-calling, structured-output, and multi-step agentic workflows using the right model and architecture for each problem.
Own the production quality bar for what you ship: evaluation datasets and automated checks (accuracy, groundedness, task completion, safety, cost etc.)
Work directly with business owners to map processes, test with users, and drive each pilot to a clear scale-or-stop decision.

our company's product is built around an AI agent that security analysts and detection engineers work with directly. It investigates coverage questions against live enterprise security data, authors and tests detection logic, and tunes noisy alerting.
That agent is already in production with enterprise design partners. Now we need to make it dependable and scalable enough for GA.
You will own that evolution: the agent architecture, its evaluation and quality system, and the production engineering around it. This is a hands-on senior IC role with real architectural authority - you set the technical direction and you write the code.
The agent operates inside customer security environments, where a wrong action can become a customer incident. Correctness, isolation, observability, and evaluation are not polish. They are the product.
What you'll be doing
Agent architecture: Design the evolution from today's production single-agent system to a multi-agent one: orchestration, task decomposition, runtime and framework choices, and a migration path that does not break what design partners already rely on.
Agent capability: Own the prompts, context, skills, and tool design that make the agent genuinely good at detection engineering across multiple security platforms, not just plausible-sounding.
Evaluation platform: Build the harnesses, judges, and golden datasets that turn "the agent feels better" into a number, plus the CI gates that keep regressions from shipping.
Reliability and safety: Keep long-running agentic sessions healthy in production, and build the isolation and guardrails required of an agent working inside enterprise security environments.
Production debugging: Work real failures from production traces, and turn each one into an eval case that can never regress silently.
Technical direction: Make the calls on architecture, sequencing, and quality bar and be accountable for the outcome, including raising how AI-natively the whole team builds.
Cross-team partnership: Partner with product and customer-facing teams on what the agent should do, and with platform teams on the data and integrations it depends on.

We are looking for a Product Manager to shape the future of business travel through Book with AI. You will own the product across Hotels, Rental Cars, and Rail, while serving as the day-to-day product lead for the team and making complex bookings feel as natural, trustworthy, and seamless as working with an expert travel agent.
We are looking for a Product Manager to own the Book with AI product end to end across Hotels, Rental Cars, and Rail. Your mission is to make booking complex business travel feel as natural as working with an expert travel agent - while making sure the agent is accurate, policy-aware, transparent, and reliable enough for real transactions.
This is a highly cross-functional, hands-on role. Youll lead day-to-day execution across the entire traveler journey - from the initial request through search, comparison, booking, post-booking support, and recovery when things go wrong, partnering closely with engineering, design, applied AI/ML, data, travel domain experts, operations, and other product teams.
Youll shape the conversational and user-interface experiences that power Book with AI, helping travelers express their needs, evaluate trade-offs, make confident decisions, and complete bookings with less effort. Youll stay close to the customer experience by reviewing real conversations, leading bug bashes, interpreting evaluation results, identifying patterns in user feedback, and ensuring the team remains focused on the highest-impact opportunities.
The ideal candidate thrives in ambiguity, learns quickly, and is energized by AI-native products. They create clarity and momentum without waiting for perfect information, roll up their sleeves, and drive work through uncertainty and chaos.
What You'll Do:
Own the vision, strategy, roadmap, and execution for Book with AI across Hotels, Rental Cars, and Rail.
Own the full Book with AI journey: intent understanding, search, ranking and recommendations, policy and personalization, comparison, contract, checkout, booking, and post-booking support.
Define a coherent cross-LOB experience for users who want to plan and book multiple parts of a trip, including clear sequencing, handoffs, and expectations.
Improve the product's key outcomes, including successful booking completion, conversion, time to book, user effort, CSAT, trust, and repeat usage.
Lead experimentation across conversational flows, recommendations, search and booking surfaces, using clear hypotheses and measurable success criteria.

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless company ronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

At our company, our team is the foundation of our success. Join a people-centric culture that is fast-paced and rewarding: youll have the opportunity to work with some of the most talented people in the industry who value innovation, diversity, integrity, and collaboration.

Who we seek:

We are looking for a Senior Product Manager - Agentic AI to help shape and build our AgentIQ, our agentic AI platform transforming how enterprises understand their data, automate workflows, and move from insight to action.
This is a hands-on product role for someone who has built and shipped AI-powered products and, ideally, AI agents or agentic workflows. You will work closely with Engineering, AI/ML, Design, and customers to turn emerging AI capabilities into reliable, intuitive, and valuable enterprise products. You will own product initiatives end-to-end: from identifying customer problems and validating use cases to launching capabilities and measuring impact.

What you'll do:

Build the future of AgentIQ: Define and execute product initiatives that use AI agents to solve real enterprise data intelligence problems.
Design agent experiences: Define agent goals, workflows, tools, APIs, skills, actions, decision points, and human-in-the-loop experiences.
Own execution & trust: Translate concepts into requirements, user stories, and acceptance criteria while defining permissions, guardrails, explainability, and failure handling for enterprise data actions.
Customer discovery & validation: Work directly with customers to identify high-value agentic use cases, validate prototypes, gather feedback, and translate needs into product improvements.
Conduct market research: Analyze customer needs, competitive offerings, market trends, and emerging agentic AI technologies to identify opportunities and shape AgentIQs product strategy.

This role owns the AI-native GTM engine: the systems that find, enrich, reach, and route prospects and customers, and the workflows that create new opportunities for the company. You build revenue systems with AI tooling, then run them against pipeline targets. The scope is wide on purpose. You own the motion and the measurement, so theres no gap between what you build and what you can prove.

What youll own
Outbound engine. Multi-channel outbound end-to-end: list building, segmentation, messaging, sequencing, launch, and A/B iteration across email and LinkedIn. AI-driven personalization at scale, moving prospects from first touch to booked meeting with minimal manual input from sales.
Inbound conversion. Everything between the first visit and the booked meeting: on-site conversion paths, forms, chat, scoring, routing, and speed-to-lead. Instrument every step, find where intent leaks, and close the gap with automation instead of headcount. No inbound lead should sit waiting for a human to qualify it.
CRM truth and attribution. The measurement layer under everything else. If outbound, inbound, AEO, and content cant be attributed, none of it can be optimized or defended. Own data quality across HubSpot and Salesforce.
Data and enrichment. Prospect identification and signal tracking: enrichment waterfalls, buying signals, social listening, and clean push architecture into the CRM.
AI agents and automation. Claude is a teammate here, not a chatbot. Build the agents, skills, and workflows that encode our GTM playbooks and kill anything manual.
AI content and discovery. Build the writing agents that produce inbound at volume: programmatic pages, competitor comparisons, persona and vertical landing pages, localized variants, and one asset turned into fifteen across channels. AEO is the other half of this. Buyers now start in ChatGPT, Claude, Perplexity, and AI Overviews, not on a results page, so the content has to be built to be retrieved and cited: structured content and schema, presence on the review sites and communities models pull from, and a technical layer that makes our legible to crawlers.
Customer expansion. The signal engine that finds revenue inside the base: segmentation, expansion triggers, and routing each account to the right owner or the right sequence at the right time.
The stack itself. The GTM tech stack end-to-end: evaluate, buy, integrate, and kill tools as the motion evolves, and build tools with direct impact on revenue when nothing off the shelf does the job.

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

We are looking for an R&D Solutions Engineer to join the Agentic AI team within our companys AI for Security organization. This is a customer-facing role embedded directly within R&D, with a dotted-line relationship to the broader Solutions Engineering organization. You will work closely with R&D, Product, Sales, and SE teams to bring new Agentic AI capabilities into the hands of customers early, learn from real-world usage, and help turn emerging capabilities into products and sales motions that can scale.
You will report directly to the Director of Agentic AI within the AI for Security organization.
This is not a traditional presales SE role. Your primary focus will be building and managing relationships with design partners and Early Access (EA) customers, driving them toward meaningful product usage, and creating a tight feedback loop between customers and the teams building the product. You will also play a key role in taking capabilities that initially require direct R&D involvement and making them repeatable for the broader our company SE organization.
Key Responsibilities
What You'll Do
Own Design Partners and Early Access Customers
Identify and recruit strong candidates for design partnerships and Early Access programs, working closely with Sales and SE teams.
Own the technical relationship with design partners and EA customers from initial engagement through deployment and ongoing usage.
Build and maintain a healthy portfolio of actively engaged design partners.
Understand customers environments, workflows, security requirements, and success criteria.
Help customers deploy, configure, integrate, and evaluate emerging Agentic AI capabilities.
Drive customers toward meaningful real-world usage rather than isolated demos or proofs of concept.
Track adoption, outcomes, blockers, and feedback across the design partner and EA portfolio.
Maintain ongoing relationships with customers to ensure early enthusiasm turns into sustained engagement and actionable product feedback.
Bring the Field into R&D
Develop a deep understanding of how customers use the product and where they encounter friction.
Translate customer conversations into concrete product requirements, technical insights, and prioritization signals for R&D and Product.
Identify recurring patterns across customers rather than treating every request as a one-off.
Work directly with engineers and product leaders to reproduce issues, validate assumptions, and test potential solutions.
Regularly synthesize customer feedback into clear recommendations for R&D and Product.
Help the R&D organization understand what is required for new capabilities to succeed in real production environments.
Make New Capabilities Sellable and Repeatable.