Design And Cost Estimation Head in United States

Datadog SQL (DDSQL) and Sheets are two of the newest products within Graphing, Datadog’s most-used product area. Together, they give customers flexible ways to query, combine, analyze, visualize, and share data across Datadog. This role owns the product direction and user experience for both products. Your goal is to help users move from raw data to trustworthy answers quickly—whether they prefer writing SQL, working in a spreadsheet, or building visual analyses. Doing this well requires more than adding analytical features. Real-world analysis involves nuanced decisions about data models, joins, aggregations, time windows, missing data, query performance, and the transition from exploration to reusable work. You’ll work closely with design and engineering to make these capabilities understandable without limiting their analytical power. You’ll also help expand the data customers can analyze in Datadog, including third-party and business data alongside operational telemetry. This will make DDSQL and Sheets central analytical tools for a broader range of questions and users. What you'll do: Own DDSQL and Sheets end to end: roadmap, adoption, and growth strategy Partner closely with design to establish a high bar for information architecture, interaction design, and visual polish across complex analytical workflows Use product analytics and customer research to identify friction, improve onboarding, and measure whether users are reaching useful answers faster Define the experience for querying, transforming, visualizing, and sharing data, from query composition and results exploration to errors, performance feedback, and collaboration Define and execute the strategy for bringing third-party data into Datadog: from market research and use-case definition through pricing Expand Datadog SQL from a standalone editor into a platform-wide query capability on all graphs Redesign how users discover and get started with Graphing products: rethink list pages, build onboa

Senior Product Manager - Search Datadog’s Search team helps users – both human and agents – find answers to their questions. Search is a critical function in Datadog, and touches many product surfaces: the query editors in product homepages and dashboards, search bars for finding relevant assets, the global cmd+k navigation search, and the MCP tools that our Bits AI agent uses to respond to natural language prompts. Search is a full-stack team: owning user-facing search components, backend search ranking systems, and machine learning models to produce recommendations. The Search team is relatively new, and still growing. We have recently built out agentic search tools, and we are looking for a leader to help us expand to ambitious orchestration systems that deliver accurate results, and proactive recommendations across both keyword and semantic search. Beyond this, you’ll have room to influence how Datadog thinks about Search as a strategic surface. What you’ll do: Define and deliver how Datadog's AI agents discover the right context and tools to answer natural-language questions accurately and at scale Stay on top of industry trends in UI and agentic search experiences and capabilities Collaborate with Applied AI teams to integrate ranking, personalization, and recommendation models that scale across both human and agentic users Define and monitor KPIs for search quality, adoption, and downstream impact on user productivity; use them to drive data-informed decision making Engage directly with customers and internal product teams to deeply understand search journeys across query editors, global navigation, and natural-language agent interfaces Who you are: You have experience with search, ranking, or recommendation systems You are familiar with or very interested in MCP servers and differences between human and agentic UX You have a sharp eye for design and strong opinions on the micro-interactions — keyboard navigation, autocomplete behavior, loading

Datadog is looking for an experienced Senior Account Based Marketing Manager to drive new customer acquisition and pipeline generation across the Americas (North America and LATAM), with a focus on our Commercial (SMB) segment. In this role, you will design and execute an integrated, multi-channel marketing account based-marketing strategy, spanning paid search, paid social, dedicated field programs, sponsored events, and third-party digital channels, in close partnership with sales leadership. This is a full-funnel, cross-channel mandate, with the latitude to prioritize the channels and programs that deliver the greatest impact. 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: Develop and execute a comprehensive, multi-channel marketing strategy to drive pipeline growth across the Americas, leveraging sponsored events, paid search, paid social, field-led programs, third-party digital placements, and more. Support the design and execution of 1:many, 1:few, and 1:1 ABX marketing programs for the Commercial (Small and Midsize Business) segment, combining digital tactics, such as paid social and programmatic display, with dedicated field plays, such as direct mail, to break into and expand high-value accounts. Serve as both strategist and operator for the region: help define the marketing plan and lead execution across paid media, field marketing, and event teams. Architect the operating model and playbooks for dedicated programs and partner with cross-functional marketing teams to execute dedicated programs where regional support is needed, maintaining consistent quality and cadence across the Americas. Establish the reporting and measurement framework needed to evaluate program and channel performance, refining the strategy based on results. Maintain flexi

We are a team of engineers that translate our real-world experience to help our user communities solve problems. With a focus on AI-accelerated workflows and next-generation developer ecosystems, you will have the opportunity to meet fast-moving teams where they are, helping them lay strong engineering foundations and broadening your impact to the developer community at large. This is a unique opportunity to use both your engineering depth and authentic storytelling skills to shape how the next wave of builders approach software health, scalability, and observability. What You’ll Do: Help developers hone their craft in an AI-accelerated world by exploring how AI coding assistants and rapid-prototyping tools change software workflows, and guiding teams on how to balance rapid prototyping with established engineering practices around performance, code health, and system reliability. Build in public by creating authentic, peer-to-peer technical content, sharing your engineering insights directly where modern developers collaborate in person and online. Drive a constructive, bidirectional feedback loop between fast-moving developer communities and our internal teams, translating real-world developer experiences into actionable product insights to shape our roadmap while advocating for user needs from the inside. Design and ship high-quality open-source boilerplate templates, quickstarts, and tools that make integrating observability seamless across AI native workflows, next-gen platforms (Vercel, Supabase, etc.), and cloud providers (AWS, GCP, Azure). Who You Are: A builder with approximately 10+ years of software engineering experience, ideally having shipped applications from scratch or worked within an early-stage startup environment where you've worn multiple hats. An AI-fluent developer who natively leverages AI coding assistants and rapid-prototyping tools as a core part of your regular, day-to-day development workflow. Multi-stack familiar, comfortable writing an

Join the MongoDB Server Query Optimization team, and help us build a world-class distributed open-source query optimizer. Our team plays a crucial role in the experience and performance of data processing. We are responsible for the MongoDB Query Language and the lifecycle of each query, through parsing, optimization and plan selection. We have a presence across the US and Europe including New York, Dublin, Seattle, Palo Alto, and Chicago. We support office-based and remote work and align projects with convenient work hours for each time zone. We have tons of interesting problems to solve with a direct impact on users for transactional, time-series, and analytical workloads. The team is endeavoring to systematically rewrite every major component of our optimization and execution systems. We need your help to design and build the heart of a distributed, flexible schema, document database. ​​This role can be based out of our US offices or remotely in the North America region. Candidate Profile 10+ years of experience in data management systems, distributed systems, or large-scale backend engineering Experience with building production-level code with a large user base, robust design structure and rigorous code quality Degree in Computer Science or similar field, or equivalent practical experience, with strong competencies in data structures, algorithms, and software design/architecture Experience with large code bases written in C++ or another systems programming language. You'll need to trace down defects, estimate work complexity, and design evolution and integration strategies as we rewrite different components of the system A strong foundation in core database internals is essential. While direct experience in query optimization is a massive bonus, it is not a prerequisite. We are also excited to meet candidates with strong backgrounds in compilers, language transpilers, or distributed storage systems Position Expectations Innovate in the area of flexible schema d

The Opportunity MongoDB’s partner ecosystem — systems integrators, ISVs, and the major cloud providers — is a strategic growth engine for the business. We’re looking for a Senior Manager, Sales Plays and Offerings to design the joint go-to-market motions that turn partner relationships into pipeline and revenue. This is a highly cross-functional, strategic role: you’ll build the sales plays and joint offerings themselves, partner with Enablement to get the field and our partners ready to sell them, instrument how they perform, and work with the Programs lead to make sure incentives reward the behavior we want to see. You will report directly to the VP of Partner Strategic Operations and act as a connective layer between Partnerships, Sales, Enablement, and Programs — translating ecosystem strategy into repeatable, measurable, field-ready motions. We are looking to speak to candidates who are based anywhere in the US for our hybrid working model. What You'll Do Build sales plays and joint offerings Design and package partner sales plays and joint solution offerings with priority ISVs, SIs, and cloud partners — defining the joint value proposition, target segment, competitive positioning, and playbook for how field and partner sellers execute it Partner with Product Marketing, Solutions Engineering, and partner counterparts to validate technical integration stories and translate them into a compelling, sellable narrative Prioritize which plays to build and scale based on market opportunity, partner readiness, and alignment to MongoDB’s strategic pillars (e.g., AI, migrations, industry verticals) Own the lifecycle of each play from concept through launch, iteration, and eventual retirement or refresh Drive partner and field enablement Partner closely with the Enablement team to translate each sales play into field- and partner-facing assets: pitch decks, battlecards, demo scripts, certification conte

About Team Our Robotics team is focused on unlocking general-purpose robotics and advancing toward AGI-level intelligence in dynamic, real-world environments. Working across the full model and systems stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the physical constraints of real-world systems to improve people’s lives. About Role We are building a supply-chain organization capable of supporting our transition from laboratory-scale development to factory-scale operations. We are looking for an Inventory Manager to build and operate our inventory function from the ground up. This is a highly hands-on role. In the near term, you may start with an empty room and be responsible for determining what racks, shelving, bins, labels, scanners, workflows, and systems are needed to turn it into a functional stockroom. You will receive material, organize inventory, perform counts, move parts between buildings, resolve discrepancies, and establish the processes others will eventually follow. As we grow, you will have the opportunity to develop this foundation into a full-scale, multi-site factory inventory operation. You may hire and manage contractors or onsite inventory administrators, but this role will initially have a significant individual-contributor component and will remain accountable for day-to-day execution. This role owns inventory and internal logistics. It does not own purchasing, production planning, or inbound and outbound supplier logistics. This role is based in San Francisco, CA and requires in-person presence 5 days a week. In this role you will: Build inventory operations from the ground up across various OpenAI facilities. Design and set up stockrooms, receiving areas, and material-storage locations, including selecting racks, shelving, bins, carts, labeling equipment, scanners, and other infrastructure. Personally execute core inventory

About the Team The CoT Monitorability team at OpenAI studies whether and when the chain-of-thought of frontier reasoning models is monitorable enough to support scalable oversight. We study how to measure monitorability , which training mechanisms affect monitorability, and speculative methods to improve monitorability. While we mostly focus on CoT monitorability at the moment, we care more generally about any form of monitorability, auditing methods, and improving alignment. We were the first to show that chain-of-thought monitoring can be a practical additional safety mechanism, and today our monitoring systems are actively used on OpenAI’s largest RL training runs to detect misbehavior. The issues we surface are then used to help improve our reward functions, environments, etc (without directly training against a CoT monitor). Our work sits in Alignment and intersects with model training, alignment evaluations, monitoring, and frontier-risk research.We care most about monitorability where the stakes are high, and about preserving useful oversight signals as models become more capable. About the Role We’re looking for a researcher with strong empirical ML expertise and a deep interest in model behavior, alignment, or interpretability. Direct chain-of-thought interpretability experience is welcome but not required; strong candidates may come from broader interpretability, alignment, model training, or investigative model-behavior work. As a researcher on the Alignment team, you will design and run experiments that improve our understanding of model monitorability. You will investigate how training interventions across the model-development pipeline influence whether reasoning remains legible, build evaluations that make those questions measurable, and help translate findings into practical oversight and training recommendations. You may also help develop new monitoring models or methods and apply them to OpenAI’s largest training runs. This role is especially well

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We're seeking a Security Engineer to join our First-Party Hardware team. In this role, you will own the end-to-end security foundation for OpenAI's first-party AI hardware systems, working across hardware security, embedded security, system security, and practical deployment at data center scale. You will partner with silicon, hardware, firmware, infrastructure, manufacturing, operations, and security teams to define and deliver system-level device trust. This includes boot integrity, device identity, provisioning, attestation, management-plane security, storage encryption, debug controls, firmware update and recovery, RMA, and decommissioning. You will be accountable for turning threat models into requirements, requirements into implementation, and implementation into validation evidence that can support launch decisions. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role, you will: Own security requirements, threat models, validation strategy, and launch-readiness evidence for first-party hardware platforms from early design through production deployment. Design and review secure boot, measured boot, roots of trust, platform firmware resilience, firmware signing, recovery, and anti-rollback strategies across heterogeneous devices. Own device identity, provisioning, enrollment, attestation, certificate lifecycle, and key-management requirements across manufacturing and data center bring-up. Harden management

About the Team The Codex Web Layer team provides the web-based systems and user experiences for Codex across the entire stack, from the Electron-like application framework that powers the application, to the user-facing in-app browser. About the Role In this role, you will be responsible for designing and implementing infrastructure and features end-to-end for the Codex desktop client application. You will help define what it means to be a hybrid agentic/interactive web browser. The team embodies “full stack” development from the lowest-level OS integration to the highest-level interaction design. This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role you will: Partner closely with product and design to conceive, design, and build features for Codex web browsing features on macOS and Windows. This role will focus mostly on the backend C++ layer and Chromium, but many features cross the full stack including some TypeScript. Partner with the wider Codex team to deliver a high-performance, stable, and secure application platform for client development. This includes API design and implementation (mostly in C++) and the infrastructure that supports deploying it (in Python, TypeScript, and agentic skills). Work with a small, experienced team of engineers on this critical and rapidly growing product. You might thrive in this role if you: Have significant experience building technically complex features end-to-end. Are a strong C++ developer, especially with experience in browser environments like Chromium and Electron. Since this role is more backend focused, general knowledge of web development and TypeScript is helpful but not required. Thrive in a fast-paced, ambiguous environment. Communicate clearly and concisely across many different roles in the organization. Are self-directed, identifying important work and executing it end-to-end. About OpenAI OpenAI is an AI

About the Team The Agent Infrastructure team at OpenAI is responsible for building systems that enable training and deployment of highly useful AI agents, both internally and for the world. We work hand-in-hand with researchers to design and scale the environment in which agentic models are trained – providing a workspace for AI models to execute code, debug issues, and develop software just as human SWEs do. Our training environment for agentic models operates at an extremely high scale and has the flexibility to emulate any environment in which an agent might work. At the same time, our team builds and maintains OpenAI’s core platform for the deployment and execution of agents in production. Our systems power products such as Codex, Operator, tool use in ChatGPT, and future agentic products. Some of the most challenging technical problems in scaling the capabilities and utility of agents and agentic models lie in the infrastructure layer – and our team is focused on building the research and production systems that enable OpenAI to train the most capable models in the world, and maximize the utility of our agentic products for users around the world. About the Role As a Software Engineer on the Agent Infrastructure team, you will have the opportunity to work closely with both research and product at OpenAI - building and scaling systems to train highly capable agentic models, and building the platform and integrations to launch new agents to hundreds of millions of users worldwide. Your work will consist of both building new capabilities - standing up the infrastructure and integrations needed to train more complex agentic models - and rapidly scaling these new capabilities to some of the largest compute clusters in the world. At the same time, you’ll be instrumental to the launch of agentic products at OpenAI - building, maintaining, and scaling the production platform on which all agents run. We’re looking for people with deep experience building AI infrastructu

The Fleet team at OpenAI supports the computing environment that powers our cutting-edge research and product development. We oversee large-scale systems that span data centers, GPUs, networking, and more, ensuring high availability, performance, and efficiency. Our work enables OpenAI’s models to operate seamlessly at scale, supporting both internal research and external products like ChatGPT. We prioritize safety, reliability, and responsible AI deployment over unchecked growth. About the Role The Software Engineer, Operating Systems & Orchestration will focus on building systems to manage hardware, configurations, vendors, and the people interacting with our infrastructure. You will design and develop solutions that integrate individual nodes and servers into unified clusters, directly contributing to advancing AI research by streamlining the overall research user experience. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Design and build systems to manage both cloud and bare-metal fleets at scale. Develop tools that integrate low-level hardware metrics with high-level job scheduling and cluster management algorithms. Leverage LLMs to coordinate vendor operations and optimize infrastructure workflows. Automate infrastructure processes, reducing repetitive toil and improving system reliability. Collaborate with hardware, infrastructure, and research teams to ensure seamless integration across the stack. Continuously improve tools, automation, processes, and documentation to enhance operational efficiency. You might thrive in this role if you: Have strong software engineering skills with experience in large-scale infrastructure environments. Possess broad knowledge of cluster-level systems (e.g., Kubernetes, CI/CD pipelines, Terraform, cloud providers). Have deep expertise in server-level systems (e.g., systems, containerization, Chef,

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: Drive the statistical direction and technical strategy for OpenAI’s experimentation platform Design and improve experimentation methodolo

About the Team Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprise and developers alike to use and access our start-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference. About the Role We are looking for an engineer who wants to take the world's largest and most capable AI models and optimize them for use in a high-volume, low-latency, and high-availability production and research environment. In this role, you will: Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production. Work alongside researchers to enable advanced research through awesome engineering. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack. Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues. Optimize our code and fleet of Azure VMs to utilize every FLOP and every GB of GPU RAM of our hardware. You might thrive in this role if you: Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference. Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done. Have at least 5 years of professional software engineering experience. Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink, etc. Have experience architecting, building, observing, and debugging production distributed systems. Bonus point if worked on performance-critical distributed systems. Have need

About the Team OpenAI's Research Data Team exists to accelerate the evaluation, safety and capabilities of our models and products. Made up of technical operators and software engineers, we design the methods in which we acquire and create data. About the Role As a Research Program Manager (RPM), Data Acquisition, you will partner with research, engineering, and operations to design and implement pragmatic solutions for acquiring data. You will be a key interface between our research roadmap and external data offerings. This role is based in our San Francisco HQ and will be part of a team of RPMs pushing the frontier of data acquisition. In this role, you will: Partner deeply with research: Work with researchers to scope data needs, define success criteria, and translate priorities into clear execution plans. Shape the data acquisition pipeline: Identify, evaluate, and advance high impact data opportunities - balancing research value, feasibility, quality, and responsible execution. Unblock yourself: Move work forward even when the path is unclear — using technical judgement, creative problem solving, and scrappy execution to make progress while longer-term solutions are still forming. Build lightweight systems and visibility: Use SQL, Python, dashboards, and simple tooling to track performance, quality, and blockers. Drive technical roadmaps: Collaborate with engineers to enhance data platforms, resolve blockers, and ensure security best practices such as access management. Scale your impact: Equip vendors and internal teams with the context, standards, and operating rhythms needed to focus on the most important problems. You’ll thrive in this role if you: Are proficient in SQL and Python for analysing datasets, querying databases, building dashboards, and generating actionable insights. Are comfortable using APIs, automation, and AI tools such as Codex to accelerate workflows, remove manual overhead, and upskill quickly in unfamiliar technical areas.Experience sou