Senior Product Manager for our AI Lab

Datadog

Today, everyone from PMs to Sales to SREs uses AI to query their business data - but every answer is a one-off with no governance, no consistency, and no visibility for data teams. We're building the system that fixes this: a context layer that learns from existing data tools driving high quality and consistent answers, an AI-powered query experience, full governance for data teams, and rich analysis surfaces to present and share the results. You'd be building a zero to one product of what we believe will become a major new product line for Datadog. At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. WHAT YOU'LL DO * Drive the product vision and roadmap spanning the Data agent experience, the semantic/context layer, governance console, chat experience and analysis surfaces (dashboards, notebooks, sheets) * Own the end-to-end product lifecycle from discovery to launch, working with a cross-functional team of engineers, designers and other PMs * Deeply understand the needs of two key personas: data teams (who govern and curate) and data consumers (PMs, engineers, SREs, executives who ask questions) * Design the context and governance layer that makes AI-powered analytics trustworthy - including auto-generation from existing BI tools, eval frameworks, confidence scoring, and self-improvement loops * Define how observability data and business data come together to serve unique use cases * Engage directly with early customers and internal dogfooding users to iterate on accuracy, usability, and trust * Independently research the competitive landscape across legacy BI vendors, warehouse-native analytics, AI-first startups, and AI labs * Work on the product pricing * Work with GTM teams to define positioning, packaging, and the path to displacing entrenched tools WHO YOU ARE * 5+ years of experience in B2B SaaS Product Management, with at least some of it spent on data-intensive or analytics products * You have domain knowledge of business intelligence, data visualization, semantic layers, data warehouses and the modern data stack. * Strong analytical skills - you can write SQL, interpret complex data, and use data to drive product decisions * Experience with AI/LLM-powered products is a strong plus - you understand prompt engineering, evals, and the unique UX challenges of non-deterministic systems * You are AI-pilled. You live in Claude Code or Cursor, have built unique AI workflows for yourself, hack around with new tools. etc * You have a strong sense of ownership with an entrepreneurial mindset - this is a 0-to-1 product that requires someone comfortable with ambiguity and speed * Sharp communication skills with the ability to interact with technical stakeholders and executives alike. * Bachelor's Degree in Engineering, Computer Science, Data Science, or equivalent experience is a plus Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: * Generous and competitive benefits package * New hire stock equity (RSUs) and employee stock purchase plan * Continuous career development and pathing opportunities * Employee-focused best in class onboarding * Internal mentor and cross-departmental buddy program * Friendly and inclusive workplace culture Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.   #LI-Hybrid Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan. The reasonably estimated yearly salary for this role at Datadog is: $192,000—$240,000 USD ---------------------------------------------------------------------------------------------------------------------------------- About Datadog:  Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center. ---------------------------------------------------------------------------------------------------------------------------------- Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.  Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.  Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.

Maze

SUMMARY OF THE ROLE: Maze is building an AI-native vulnerability management platform. Our autonomous agents investigate, triage, and remediate security findings the way a senior analyst would, only faster and at scale. As Head of AI, you'll own the intelligence that makes those agents work: the AI research and implementation strategy for the whole company, plus the crown-jewel technical problem underneath it. Our investigation agents run multi-step, non-deterministic trajectories across a toolset of 180+ tools, tested against a ground-truth exploit lab we built for exactly this purpose. Knowing whether they're getting better, and making them better, is the most important technical problem at Maze. It's the heart of this role. This is a hands-on leadership role, not a management layer. You'll set AI direction as a member of the engineering leadership team reporting to the CTO. But you'll spend most of your time building: designing evaluation frameworks for non-deterministic agents, running fine-tuning and model-routing experiments against real data, prototyping new techniques and getting them into the product. You'll lead a small, strong AI team (3–4 engineers today) by setting the technical bar and doing the work alongside them, while working closely with our ML tech lead and the product teams building agents day to day. Your impact comes from what you ship, not the size of your org. We're not looking for someone to run a large team from two layers up. We're looking for someone who wants to define how generative AI transforms cybersecurity and keep their hands on the code. This role suits a deep LLM-era practitioner who has shipped agentic systems to production, can reason about transformer internals and fine-tuning from first principles, and moves fast. We're a three-product company with a lot of surface area, a well-funded Series A (Theory Ventures) behind us, and a Series B on the horizon. The AI foundation you set now becomes the moat we compete on for years — this is a foundational hire whose standards will shape Maze's AI trajectory well past this raise. YOUR CONTRIBUTIONS TO OUR JOURNEY: * Own AI strategy and research direction: Set the technical roadmap for our AI capabilities. Stay ahead of the research curve to find, validate, and prioritise the techniques that differentiate Maze, and turn what's real into a concrete, sequenced roadmap while discarding the hype. * Own agent quality and evaluation: Build and run the frameworks that tell us whether our investigation agents are improving. That means trajectory evaluation, ground-truth scoring against the exploit lab, and end-to-end benchmarks for non-deterministic, multi-step behaviour. This is the core problem of the role. * Build the breakthroughs yourself: Prototype a new technique in days, get it into the product, and measure the impact. You'll spend most of your time hands-on in the codebase, acting as the technical product manager who guides it to production. * Run fine-tuning and model experiments on real data: Own fine-tuning pipelines, context engineering, model migration, and cost/routing optimisation grounded in production data, not proofs of concept. * Guide prioritisation across the AI team: New techniques, papers, and ideas surface constantly. You'll be the filter deciding which of them are actually worth a prototype this week, and which are noise - keeping the team focused on what moves the needle. * Lead a small team by doing: Set technical direction for the AI engineers, raise the bar through pairing and review, hire as we scale, and stay close enough to the work to make the hard architectural calls yourself. * Partner with the CTO and engineering leadership: Turn the AI roadmap into shipped capability, and make sure evaluation is wired into how the whole team builds. * Get in front of customers: Occasional direct customer exposure, translating what security teams need into concrete improvements to the ML pipeline. * Set the pace: Ship prototypes in days, not quarters. Bring urgency to a domain where most of the field still moves slowly. WHAT YOU NEED TO BE SUCCESSFUL: * Hands-on technical leadership: A track record of leading AI work while personally building it. Strategy and implementation. You lead from the front. If you've moved permanently into management and stopped shipping, this isn't the right fit. * Shipped LLM/agentic systems to production: You've built and run generative-AI systems that real customers use, not research prototypes or slideware. You can point to agents or LLM features you put into production and improved over time. * Deep LLM-era technical depth: You can explain transformer architecture, training, fine-tuning (e.g. LoRA), and inference from first principles. We test this directly. A strong pre-LLM ML pedigree (RL, NLP, recommendations, ASR) is valuable but won't substitute for modern generative-AI depth. * Built evaluation frameworks for non-deterministic systems: You've designed and run evals for multi-step, non-deterministic agents: trajectory evaluation, LLM-as-judge, fine-tuning result measurement. This capability is rare and it's the one we most want. It will set you apart. * Top-tier pedigree with a builder's edge: Experience at a leading AI organisation or strong AI-native startup where you raised the technical bar rather than coasted on the brand. * Unambiguous startup signal: You've operated at early stage or built something from zero. You move fast, own outcomes end-to-end, and don't need a large org around you to ship. Founder experience is a strong plus. * Pace and urgency: You ship prototypes in days. You make pragmatic calls on models, cost, and scope to keep momentum, and you're impatient with quarters-long cycles. * Sharp, concise communication: You communicate clearly and tightly in a remote-first, English-speaking team, in writing and live. You get to the point. * Nice to Haves: * Security, vulnerability-management, or adversarial-domain background. Strongly preferred. Every candidate we've rated highly has had it. Offensive security, vuln management, threat detection, or applying AI to security problems all count. * Comfort in front of customers, able to translate agent behaviour and capability into terms a security team understands. * Model cost/routing pragmatism: real experience cutting inference cost and migrating between models in production. * Track record at a successful AI-first startup, scaling a system from experimentation to production impact. * PhD or published work in ML/AI at top-tier venues, paired with real production experience. YOUR FIRST 90 DAYS * Days 1–30: Get fully up to speed on every agent we've built and how our ML evaluation pipeline works today. Start drafting a short, mid, and long-term technical plan. * Days 31–60: Ship something fundamentally new — for example, fine-tune a small model and get it into production. * Days 61–90: Move onto bigger bets — RLHF for specific use cases, scalability of the evaluation approach, and deeper customer-facing model tuning.   THIS ROLE IS NOT FOR: * Manager-of-managers or 2nd/3rd-line leaders who direct rather than build. * Fractional, advisory, or part-time profiles. * Research-only backgrounds without production shipping experience. WHY JOIN US: * The hardest problem in the field, unsolved. Evaluating non-deterministic, multi-step agents against ground truth is an open problem, and we've built the exploit lab and 180+ tool agent infrastructure to attack it. You'd own it, at the intersection of generative AI (LLMs and agents) and cybersecurity. * A team you'll want to be measured against. Founders and engineers from Amazon, Elastic, and Tessian. Hands-on leaders who've been part of multiple acquisitions and an IPO. Most people who join do so because of how strong the team already is. * Build the AI-native company from the ground up. A well-funded Series A (Theory Ventures) with a Series B on the horizon, early enough that you'll set the technical standards for how AI investigates security at scale. * Cybersecurity as a force for good. The work directly helps organisations stop attacks. Measurable impact, real customers, immediate feedback on what you ship. * Founding-level ownership and upside. Significant equity, a seat on engineering leadership, and a path to VP of AI as the team scales around what you build.

Showpad

About the position Showpad is building a Revenue Intelligence engine that transforms raw signals—CRM data, email threads, transcripts, and content engagement—into prescriptive AI guidance. We are looking for a technical Product Manager to own the unified data intelligence layer that powers this engine. You will define how we capture and model data, and critically, determine the best retrieval and inference strategies (Search, RAG, Knowledge Graphs, or Vector Similarity) for every use case.   Key Responsibilities * Data Modeling & Architecture: Define the canonical model for entities (deals, contacts, skills) and establish the retrieval strategy—choosing between keyword, dense vector, hybrid, or graph traversal based on latency and accuracy. * Search & RAG Ownership: Own the end-to-end RAG pipeline (chunking, embeddings, indexing). Write technical specs for retrieval layers in Meeting Prep and Roleplay AI, and define evaluation metrics (Recall@k, MRR). * Revenue Intelligence Roadmap: Drive the vision from "trusted foundation" to "prescriptive AI." Own the product logic for Deal Health and Winning Behavior models. * Platform Enablement: Act as the internal PM champion for the data platform, providing semantic primitives, event taxonomies, and data contracts to other product squads. * Discovery & Strategy: Conduct deep customer discovery and stay ahead of LLM/Search trends, translating fuzzy business questions into scoped ML problems.   Required Skills Must haves * 5+ years in PM: at least 2 years specifically owning data platforms, search, analytics, or ML products. * Retrieval Expertise: Hands-on experience shipping RAG pipelines or search systems. You understand the trade-offs between BM25, vector search, and reranking. * Data Technicality: Fluency with Datalake architectures (Delta Lake, BigQuery, etc.) and strong entity-relationship modeling skills. * ML Fluency: Ability to define features, label designs, and evaluation metrics. You can review a technical design doc alongside engineers. * Skilled at translating complex technical infrastructure into clear business value for executive stakeholders.   Nice to haves * Experience in B2B SaaS, CRM, or Sales Enablement. * Knowledge of graph databases (Neo4j, Neptune). * Prior background as a Data Engineer or Scientist.   Company Highlights:  Welcome to the new era of revenue effectiveness. The merger of Showpad and Bigtincan is creating the first AI-powered platform to strengthen the entire field selling motion. By unifying two industry-acclaimed solutions for content, readiness, engagement, and intelligence into a single operating system, we are delivering unrivaled scale and accelerated growth for our 2,000+ combined customers worldwide, including leaders in manufacturing (Dow, Dupont), healthcare (Fujifilm, Kaiser Permanente, Johnson&Johnson), CPG (Smucker’s, Mitsubishi, Stanley Black & Decker), and enterprise tech (HID, CultureAmp, LastPass). Acclaimed by analysts and adored by customers, we’re recognized as a Leader in the Forrester Wave™ Revenue Enablement Platforms, honored by Gartner as a Customers’ Choice in Revenue Enablement Platforms, and, together, endorsed by nearly 3,000 5-star customer reviews on G2. United, we’re focused on powering the next generation of field selling success through a more holistic engine that creates lasting value for our customers and a new vision for our category. Discover the revenue team outcomes we’re driving together at showpad.com.   What you can expect from Showpad We welcome every voice and are committed to building a truly inclusive environment where your differences are not just welcomed, they are celebrated. We’re building a best-in-class experience for our employees and are always identifying opportunities to encourage our team to be their authentic selves. Whether it’s additional company-wide days off, paid time off to volunteer at non-profit organisations, personal development opportunities or professional stretch assignments, you can expect Showpad to support you.  We are committed to creating a diverse and inclusive organisation and are proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, pregnancy, disability, age, veteran status, or other dimensions of identity.