Large Enterprise Account Executive Auth0 Jobs

Mid-Market Account Executive As an Account Executive on our Mid-Market Sales team, you will assist in Datadog’s overall business growth by strategically engaging and closing net-new customers across mid-to-large size organizations. Sellers follow a well-defined methodology, collaborate with internal stakeholders, identify the customer's unique needs, and clearly convey the value of the Datadog product. Mid-Market reps have the opportunity to grow their careers in Sales and continue contributing to Datadog team success. 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: Focus entirely on net-new logo acquisition Convert inbound leads to closed opportunities Leverage tools such as Sales Navigator and DiscoverOrg Strategically prospect into Chief Technology Officers, Engineering/IT Leaders, and technical end users (SRE, DevOps, DevSecOps) Multi-thread into different departments of a complex organization Collaborate with Sales Development Representatives to drive top of funnel activity Manage the full sales cycle, including negotiations and on-site technical demonstrations 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 experience, we encourage you to apply. Who You Are: Experienced in hunting and closing net new logos Turkish language preferred Able to strategically map out and break into large scale organizations Curious, motivated, and driven as a sales person Bold on the phones and creative in your emails Able to sit up to 6 hours, traveling to and from client sites via auto, train, or air, up to 30% of the time Benefits and Growth: High income earning opportunities based on self performance O

As an Account Executive on our Mid-Market Sales team (targeting customers 1,000 to 5,000 employees in size), you will assist in Datadog’s overall business growth by strategically engaging and closing net-new customers across mid-to-large size organizations. Sellers follow a well-defined methodology, collaborate with internal stakeholders & partner ecosystem, identify the customer's unique needs, and clearly convey the value of the Datadog solution. Mid-Market AE’s have the opportunity to grow their careers in Sales and continue contributing to Datadog team success. 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: Focus entirely on net-new logo acquisition Manage the full sales cycle, including negotiations and on-site technical demonstrations Identify robust set of business drivers behind all opportunities Develop a deep comprehension of customer's business Strategically prospect into Chief Technology Officers, Engineering/IT Leaders, and technical end users (SRE, DevOps, DevSecOps) Multi-thread into different departments of a complex organization Collaborate with Sales Development Representatives, Partners, & Marketing to drive top of funnel activity Apply modern sales strategy with real time signals and research using tools Sales Navigator & Demandbase 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 experience, we encourage you to apply. Who You Are: Curious and motivated to understand emerging technologies and how they intersect with core business drivers. Experienced in hunting, strategically mapping and breaking into net new logos Strong Pipeline Generation skills, bold in front of customers

Mid-Market Account Executive As an Account Executive on our Mid-Market Sales team, you will assist in Datadog’s overall business growth by strategically engaging and closing net-new customers across mid-to-large size organizations. Sellers follow a well-defined methodology, collaborate with internal stakeholders, identify the customer's unique needs, and clearly convey the value of the Datadog product. Mid-Market reps have the opportunity to grow their careers in Sales and continue contributing to Datadog team success. 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: Focus entirely on net-new logo acquisition Convert inbound leads to closed opportunities Leverage tools such as Sales Navigator and DiscoverOrg Strategically prospect into Chief Technology Officers, Engineering/IT Leaders, and technical end users (SRE, DevOps, DevSecOps) Multi-thread into different departments of a complex organization Collaborate with Sales Development Representatives to drive top of funnel activity Manage the full sales cycle, including negotiations and on-site technical demonstrations 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 experience, we encourage you to apply. Who You Are: Experienced in hunting and closing net new logos Able to strategically map out and break into large scale organizations Curious, motivated, and driven as a sales person Bold on the phones and creative in your emails Able to sit up to 6 hours, traveling to and from client sites via auto, train, or air, up to 30% of the time Benefits and Growth: High income earning opportunities based on self performance Opportunity for Presidents C

Mid-Market Account Executive As an Account Executive on our Mid-Market Sales team, you will assist in Datadog’s overall business growth by strategically engaging and closing net-new customers across mid-to-large size organizations. Sellers follow a well-defined methodology, collaborate with internal stakeholders, identify the customer's unique needs, and clearly convey the value of the Datadog product. Mid-Market reps have the opportunity to grow their careers in Sales and continue contributing to Datadog team success. 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: Focus entirely on net-new logo acquisition Convert inbound leads to closed opportunities Leverage tools such as Sales Navigator and DiscoverOrg Strategically prospect into Chief Technology Officers, Engineering/IT Leaders, and technical end users (SRE, DevOps, DevSecOps) Multi-thread into different departments of a complex organization Collaborate with Sales Development Representatives to drive top of funnel activity Manage the full sales cycle, including negotiations and on-site technical demonstrations 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 experience, we encourage you to apply. Who You Are: Experienced in hunting and closing net new logos Fluent in German required Able to strategically map out and break into large scale organizations Curious, motivated, and driven as a sales person Bold on the phones and creative in your emails Able to sit up to 6 hours, traveling to and from client sites via auto, train, or air, up to 30% of the time Benefits and Growth: High income earning opportunities based on self performance Op

The AI platform is responsible for all AI infrastructure across Datadog. Our mission is to provide tools and platforms that enable data scientists and engineers to conduct large-scale training and inference with ease. We support products such as Bits AI , LLMObs and all our AI research . As an engineering manager for the Evaluation & Annotation team, you’ll join a new and fast growing team and organization. You will support building and scaling the team, define our technical vision and help shape the roadmap. Your team will lead the charge on multiple critical technical challenges: AI model evaluation both offline and online, designing tooling and processes around human annotation, and establishing the standard around synthetics and AI generated datasets. You’ll work closely with sister teams in the AI platform organization ensuring a seamless AI development cycle. You’ll also partner with the Applied AI org and with Datadog infrastructure & tooling teams to build out systems from the ground up. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Manage and grow the Evaluation & Annotation team, directly managing 4-6 engineers Define our technical roadmap in alignment with AI platform goals and the Applied AI team roadmap. Work with our core platform teams to tailor Datadog's storage and data pipelines to our needs Create a strong team culture aligned with our engineering standards and our customer focus Participate in hands-on work: Code reviews, design reviews and some coding Who You Are: A Software Engineer at heart with a previous experience leading software engineering teams, as a tech lead or people manager Excellent leader with strong interpersonal skills, and the

The AI platform is responsible for all AI infrastructure across Datadog. Our mission is to provide tools and platforms that enable data scientists and engineers to conduct large-scale training and inference with ease. We support products such as Bits AI , LLMObs and all our AI research . As an engineering manager for the Training & Serving team, you’ll join a new and fast growing team and organization. You will support building and scaling the team, define our technical vision and help shape the roadmap. Your team will lead the charge on multiple critical technical challenges: distributed training of foundation models, serving at scale, designing the user experience. You’ll work closely with sister teams in the AI platform organization ensuring a seamless AI development cycle. You’ll also partner with the Applied AI org and with Datadog infrastructure & tooling teams to build out systems from the ground up. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Manage and grow the Training & Serving team, directly managing 10+ engineers Define our technical roadmap in alignment with AI platform goals and the Applied AI team roadmap. Work with our core platform teams to tailor Datadog's storage, infrastructure and data pipelines to our needs Create a strong team culture aligned with our engineering standards and our customer focus Participate in hands-on work: Code reviews, design reviews and some coding Who You Are: Previous experience (1+ years) leading software engineering teams, as a tech lead or people manager Strong technician with a mix of backend, data engineer and infrastructure experience who is interested in remaining a hands-on leader Excellent leader with strong

Come join and lead the Server Ingress Security team, where we are rearchitecting MongoDB Server’s ingress networking to make MongoDB clusters even more secure. This new team is building the Atlas Network Protection layer, a set of performant, security-critical services that harden MongoDB's pre-authentication attack surface and provides the ability to respond rapidly to emergent threats. We are looking for a talented Lead Engineer to join the team and be founding members, where you will play a crucial role in our multi-year roadmap. Our team champions a strong culture of inclusivity, diversity, and collaboration, and lives MongoDB cultural values every day – we value intellectual curiosity and honesty, and building together in an environment that prioritizes collaboration over competition. If you want to lead a fast-growing team that applies security and systems engineering fundamentals to protect a popular database at scale, join us! We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model. Candidate Profile 3+ years of experience managing a team of software engineers, including hiring, performance and growth management, compensation planning, and mentoring You have 8+ years of experience building production-quality systems software with large backend/compiled codebases, ideally in Rust. Bonus points for experience with performance profiling, network protocols, TLS, and connection management You have strong technical judgment that you use to effectively guide engineering decisions in security-sensitive or networking-adjacent domains You put the customer first and don't hesitate to cross team boundaries in search of the right solution Solid experience in designing, writing, testing, maintaining, and operating mission-critical software systems Bonus points Professional or advanced academic expertise in the domains of security or networking You enjoy coaching, career development, and creating growth opportunities to help your

The Infrastructure Engineering team is responsible for building and maintaining a self-service internal development platform that enables MongoDB engineering teams to reliably deploy and operate their own production services and products. We work with numerous engineering teams across the company to understand their infrastructure requirements and development workflows, develop broadly applicable self-service platform services and tooling, continuously monitor how platform services are being utilized, and look for ways to improve developer productivity through automation and education. We are big open source enthusiasts and use a number of open source tools in our stack (contributing upstream whenever possible). Some of the tools we use regularly include Go, AWS, Kubernetes, Crossplane, Terraform, Helm, Drone, Prometheus, and Grafana. However, technology is nothing without a stellar team of engineers that are focused on doing high quality work and working as a team to solve complex distributed computing and platform engineering problems. This is where you come in! We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Our ideal candidate 2+ years of experience managing and mentoring a team of 3+ engineers Has 5+ years of experience owning the design and implementation of large software/infrastructure projects Has built and operated large-scale distributed systems in cloud providers (AWS strongly preferred) Has a strong backend programming background. Fluency in Go is strongly preferred; deep experience with another compiled or strongly-typed backend language is acceptable Pragmatic, detail-oriented, self-motivated, and understands the benefits of collaboration Strong experience operating production Kubernetes clusters, not just deployed to it Has practical experience defining and operating against SLI/SLOs for services they owned Strong experience with observability tooling: metrics, logging, traces, Prometheus, Grafana, OpenTe

About the Team The Recursive Self-Improvement (RSI) team works across research, engineering, product, and infrastructure to build AI systems that accelerate and ultimately conduct high-quality research at OpenAI. We work to automate real research workflows and improve research productivity by building systems and feedback loops, designing evaluations, and training models to develop missing capabilities. Our work spans the full lifecycle of model training, evaluation, and deployment to help researchers move faster and tackle increasingly ambitious problems. About the Role We’re hiring research scientists , research engineers , and AI systems engineers to work on automating research at OpenAI. This role is based in San Francisco, CA. In this role, you will: Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution. Turn real research workflows and model failures into data and evaluation flywheels. Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training. Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure. Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows. Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments. This is a high-ownership role for researchers and engineers who thrive in ambiguity, move fluidly between research and implementation, and turn emerging opportunities into rigorous, reliable, scalable results. You might thrive in this role if you: Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems. Are a strong generalist who can move between open-ended research and practical implementation, turning ambig

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Software Engineer to join our Data Acquisition team. Responsibilities: Own and lead engineering projects in the area of data acquisition including web crawling, data ingestion, and search. Collaborate with other sub-teams, such as Data Processing, Architecture, and Scaling, to ensure smooth data flow and system operability. Work closely with the legal team to handle any compliance or data privacy-related matters. Develop and deploy highly scalable distributed systems capable of handling petabytes of data. Architect and implement algorithms for data indexing and search capabilities. Build and maintain backend services for data storage, including work with key-value databases and synchronization. Deploy solutions in a Kubernetes Infrastructure-as-Code environment and perform routine system checks. Conduct and analyze experiments on data to provide insights into system performance. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in software development. Experience with large web crawlers a plus Strong expertise in large stateful distributed systems and data processing. Proficiency in Kubernetes, and Infrastructure-as-Code concepts. Willingness and enthusiasm for trying new approaches and technologies. Ability to handle multiple tasks and adapt to changing priorities. Strong communication skills, both written and verbal. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an

About the Team OpenAI’s Forward Deployed Engineering (FDE) team turns research breakthroughs into production-grade systems. We embed deeply with customers to solve high-leverage problems and act as the delivery engine for our most complex large-scale engagements. We move quickly from prototype to production and surface reusable patterns that shape our platform. We operate at the intersection of deployment and development – working closely with OpenAI Research, Product and Partnerships. About the Role As a Technical Deployment Lead (TDL), you will define how OpenAI delivers complex systems to Semiconductor customers. You will own how solutions are scoped, built, shipped, and adopted across high-value engineering workflows such as RTL design, verification, and physical implementation. You’ll translate business outcomes into a technical plan, run day-to-day execution across FDEs, Researchers, and Customer Engineers, and partner with customer teams to ensure delivery supports their goals. You will focus on the semiconductor vertical to deploy next-generation AI capabilities. You will own delivery end-to-end: embedding with customers to map workflows and success criteria, ensuring components ship on time, and leading readiness and change management for adoption. You’ll track progress, manage dependencies, make sequencing decisions, and drive 0→1 prototypes through MVP and scale. You will also share field insights with Product and Research to guide roadmap and priorities. Success will be measured first and foremost by impact - deployments that deliver measurable value against customer goals, drive adoption, and become critical to their workflows. Additional measures of success include delivery reliability (milestones hit, low reopen/churn), operating leverage (patterns reused across deployments), judgment under pressure, and product impact (field signal that shifts roadmaps/architectures). This is a high-trust, high-autonomy role. Success requires deep technical project m

About the Team OpenAI’s Infrastructure Operations team is responsible for the availability, reliability, and operational excellence of one of the world’s largest AI infrastructure networks. The team owns day-to-day operations of production AI networks across Industrial Compute's data centers, working with colocation providers, deployment teams, and hardware vendors to deliver highly available GPU infrastructure for AI training and inference workloads. About the Role We are seeking an Infrastructure Operations Engineer to operate and improve the large-scale Ethernet fabrics that support GPU clusters, storage systems, and management infrastructure. This role combines hands-on production operations with automation, observability, and incident response across a global AI network. The ideal candidate has experience operating high-availability data center, cloud, AI, or HPC networks and can move comfortably from physical-layer troubleshooting to routing and fabric behavior, change execution, and root-cause analysis. You will partner closely with network architecture, systems engineering, GPU engineering, storage engineering, security, deployment, site operations, service providers, colocation partners, and hardware vendors to raise reliability and reduce operational toil. Key Responsibilities Own the operational health, availability, and reliability of production AI network infrastructure across Industrial Compute's data centers. Monitor, troubleshoot, and resolve network incidents while meeting service-level objectives (SLOs), reducing Mean Time to Detect (MTTD), and minimizing Mean Time to Recovery (MTTR). Operate and maintain large-scale Ethernet fabrics supporting GPU compute, storage, and management networks. Execute production network changes, maintenance windows, and capacity expansions with minimal customer impact. Manage the hardware lifecycle, including switch and optics replacements, RMA coordination, software upgrades, and preventive maintenance. Support new A

About the Team The Post-Training Frontiers team is responsible for training the frontier agents OpenAI ships to the world (GPT-Next). We train the flagship agentic models behind Codex, ChatGPT, and the API through large-scale reinforcement learning. The team’s work spans four areas. First, execution and science: working with teams across OpenAI to decide what can go into the final model and how, using scientific experiments and evals that are representative of the final pipeline so issues can be recognized early. Second, RL scaling: executing the final large-scale reinforcement learning run, making sure GPUs are used efficiently and training stays healthy. Third, research: improving horizontal capabilities like instruction following, factuality, memory, and multi-agent behavior, where the team’s broad visibility helps identify cross-cutting improvements across teams and domains. Fourth, engineering: maintaining the infrastructure stack and internal tools to ensure that both the final run and all integrations go as smoothly as possible and that the systems are easy to work with. About the Role This role focuses on keeping our frontier RL training runs fast, reliable, and unblocked. You will work across engineering and infrastructure problems as they emerge, from scaling and orchestration issues to inference bottlenecks, numerical problems, and hardware failures, as well as supporting large horizontal integrations in the big run, like multi-agent capabilities or memory. This is a role for a strong generalist who quickly learns anything needed for the task, has high attention to detail, debugs deeply, and is motivated by fixing the highest-impact problem in front of the team. In this role, you will: Keep large-scale async RL training runs moving by jumping into the most urgent engineering and infrastructure problems. Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure. Improve the reliability and efficiency of RL trai

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 Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making our models in ChatGPT and future potential products proactive in ways that are truly useful. We're laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it's helping. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models’ personalization and agentic capabilities. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a highly personalized, collaborative, and proactive assistant. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. 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: Own and pursue a research agenda to improve the proactivity and ability of our models to further user goals. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. Collaborate closely with the other research and product teams to influence the shape of technical solutions in the product You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have a working knowledge of LLM post-training and evaluation approaches Are passionate about, or have experience thinking about, personalization and enabling users to achieve their goals Are comfortable diving into a large ML codebase to debug. Thrive in a dynamic and technically complex environment. About OpenAI