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Senior Enterprise Customer Success Managerとして、Datadogの中でも最も重要度の高いエンタープライズ顧客を担当し、プロダクトの健全な活用と優れた顧客体験の実現を牽引していただきます。 Datadogでは、Enterprise Customer Success Managerと同様に、オフィスカルチャーとハイブリッドワークを重視しています。 Datadogでは、オフィスカルチャーを大切にしています。人と人との関係性やコラボレーション、そこから生まれる創造性を重視し、ハイブリッドワークを通じて、一人ひとりに合ったワークライフバランスを実現しています。 主な業務内容（What You’ll Do） SalesおよびTechnical Solutionsと連携し、オンボーディングから活用成熟フェーズまでの戦略的な活用促進を主導 新規顧客オンボーディングの全体設計およびプロジェクトマネジメント 顧客サクセスプランの策定・管理・高度化 Datadogへの投資価値・成果について、エグゼクティブレベルの意思決定者と継続的にアライン 顧客組織内で影響力を持つステークホルダーとの戦略的関係構築 • 社内のクロスファンクショナルチーム（Sales、Support、Enablement、Product、Finance、Legal）との連携 利用状況データの分析を通じた更新機会・リスクの特定 顧客活用・健全性の最終責任者としての対応 チームメンバーや他部門と連携し、ベストプラクティスの構築・展開をリード 求める人物像（Who You Are） B2B SaaSにおけるCustomer SuccessまたはAccount Managementの実務経験5年以上（技術者向け顧客対応の経験があれば尚可） 大規模エンタープライズ顧客を含むアカウントポートフォリオの担当において、豊富な実績をお持ちの方 顧客の課題・目的・優先順位を深く理解するためのディスカバリーを高いレベルで実行できる方 複雑な概念を分かりやすく伝え、顧客の課題・目的に結びつけるエキスパートレベルのコミュニケーション力かつ、相手に応じてメッセージを柔軟に調整できる方 スピード感のある環境で、自身のスキルを継続的に高めながら、チーム全体のレベルアップに積極的に関与できる方 必要に応じて顧客訪問やイベント対応のための出張が可能な方 日本語：読み書き・会話ともに流暢 英語：読み書き・会話ともにビジネスレベル以上 Datadogは、さまざまな立場の人々を大切にしています。誰もが初日から上記の資格をすべて満たすわけではないことは理解しています。それでも構いません。テクノロジーに情熱を持ち、スキルを伸ばしたいと思っている方は、ぜひご応募ください。 利益と成長： 最高のオンボーディング MEDDICとCommand of Messageの営業研修 包括的な企業文化、コミュニティ・ギルドに参加する機会 社内ネットワークのための部門内メンターおよびバディ・プログラム 継続的な専門能力開発、製品トレーニング、キャリアパスの提供 新規雇用株式（RSU）および従業員株式購入制度（ESPP） 手厚く競争力のある福利厚生パッケージ 上記の福利厚生と成長は、雇用国やDatadogでの雇用内容によって異なる場合があります。 #LI-Hybrid 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 Dat

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection , error outliers and faulty deployment analysis . As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. 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: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performance You are excited to work on

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

About Voyage AI Team at MongoDB Voyage AI team in MongoDB is building a best-in-class, general-purpose, domain-specific, and fine-tuned embedding models and rerankers to enable accurate, efficient unstructured data search and retrieval for RAG, recommendation, semantic search, and more. It is backed by a strong team of AI researchers from Stanford, MIT, Berkeley, Princeton, and CMU, who have conducted over five years of cutting-edge research on training embedding models. Voyage AI was acquired by MongoDB recently, and is now integrating the SOTA embedding models with MongoDB's data platform to create powerful end-to-end solutions. We are looking to speak to candidates who are based in Palo Alto for our hybrid working model. Position Overview We are seeking a Staff Research Scientist to join our team and contribute to the development of next-generation AI models. This position offers a unique opportunity to work on challenging problems at the intersection of machine learning research and practical deployment of large neural networks. This role can be based out of our Palo Alto office, or remotely in the United States. Responsibilities Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers Innovate in next-generation information retrieval and LLM agent paradigm Collaborate closely with other research scientists and research engineers as well as peers across the organization Qualifications PhD degree in Computer Science or related field A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications in top venues Strong background in machine learning, deep learning, and natural language processing Experience building complex neural networks for language and visual understanding Capable of conducting rigorous empirical studies to validate theoretical results Excellent leadership, problem-solving, and communication ski

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling Implement models, run experiments at scale, and profile for reliability, performance, and cost Build simulation environments and replay infrastructure for agent training and evaluation Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity Collaborate with Research Scientists, Product, and Engineeri

Role Overview Own the end-to-end technology strategy and roadmap for the Partner, Customer success, Professional Services and Customer Support organizations, translating business objectives into scalable, AI-native platforms. Lead a small team of Product Managers while staying hands-on across solutioning, architecture, and delivery. We are looking to speak to candidates who are based in Palo Alto for our hybrid working model. Key Responsibilities Partner Technology Own the Partner Center technology roadmap spanning cloud provider integrations, partner attribution & telemetry, and incentive/MDF management Drive build-vs-buy decisions for the Partner platform in direct partnership with Partner leadership Deliver solutions that support the full partner lifecycle: onboarding, co-sell, information sharing, support, progress tracking, and program management across Resellers, Technology Partners, ISVs, and Cloud Marketplaces Align partner technology with Sales, Partner Ops, and Partner Specialist workflows Customer Success Technology Own the Customer Success technology stack supporting CSMs across the full customer lifecycle: onboarding, adoption, expansion, and renewal Partner with Customer Success leadership to translate business objectives into scalable tooling and automation Enable CSM productivity through health score visibility, account intelligence, and proactive risk alerting Ensure tight integration between Customer Success platforms and Sales, Support, Billing, and Product systems Drive adoption of AI-assisted workflows for CSMs including next-best-action recommendations, sentiment signals, and churn risk indicators Customer Support Technology Own the Customer Support technology stack to enable customer support team with right tooling, including AI/agentic infrastructure, ETL/data pipelines, Workforce Management, and customer engagement tooling (chat, voice, workflow automation) Partner with Technical Support, Customer Success, and Professional Services to al

Enterprise Customer Success Managerとして、Datadogの中でも最大規模かつ戦略的に重要な顧客を担当し、プロダクトの健全な活用促進と、ポジティブな顧客体験の実現をリードしていただきます。 Datadogでは、オフィスカルチャーを大切にしています。人と人との関係性やコラボレーション、そこから生まれる創造性を重視し、ハイブリッドワークを通じて、一人ひとりに合ったワークライフバランスを実現しています。 主な業務内容（What You’ll Do） SalesおよびTechnical Solutionsと連携し、オンボーディングから活用成熟フェーズまでのプロダクト利用促進を主体的に推進 新規顧客のオンボーディングプロセスをオーナーとして設計・推進・プロジェクト管理 顧客のサクセスプランを策定し、主体的に管理・実行 Datadogへの投資価値・成果について、意思決定者と継続的に認識をすり合わせ 顧客組織内のキーパーソン・影響力のあるステークホルダーとの関係構築・維持 社内の各部門（Sales、Support、Enablement、Product、Finance、Legal）との密な連携 利用状況データをモニタリング・分析し、更新機会やリスクの早期把握 顧客のプロダクト活用および健全性（Customer Health）の最終的な責任者として対応 求める人物像（Who You Are） B2B SaaSにおけるCustomer SuccessまたはAccount Managementの実務経験3年以上 大手エンタープライズ顧客を含むアカウントポートフォリオの担当経験 顧客の課題・目的・優先順位を理解するためのディスカバリーを主導できる方 複雑な内容を分かりやすく伝え、顧客の課題・目的と結びつけて説明できる高いコミュニケーション力 また、相手に応じてメッセージを柔軟に調整できる方 スピード感のある環境で、自身のスキルを継続的に高めながら、チームのベストプラクティス形成にも積極的に関与できる方 必要に応じて顧客訪問やイベント対応のための出張が可能な方 本ポジションでは、日本国内およびグローバルの社内関係者・社外のお客様とのコミュニケーションが発生するため、日本語・英語ともに、読み書きおよび会話においてビジネスレベル以上の語学力をお持ちの方 Datadogは、さまざまな立場の人々を大切にしています。誰もが初日から上記の資格をすべて満たすわけではないことは理解しています。それでも構いません。テクノロジーに情熱を持ち、スキルを伸ばしたいと思っている方は、ぜひご応募ください。 利益と成長： 最高のオンボーディング MEDDICとCommand of Messageの営業研修 包括的な企業文化、コミュニティ・ギルドに参加する機会 社内ネットワークのための部門内メンターおよびバディ・プログラム 継続的な専門能力開発、製品トレーニング、キャリアパスの提供 新規雇用株式（RSU）および従業員株式購入制度（ESPP） 手厚く競争力のある福利厚生パッケージ 上記の福利厚生と成長は、雇用国やDatadogでの雇用内容によって異なる場合があります。 #LI-Hybrid 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

As an Enterprise Customer Success Manager, you will be responsible for driving the healthy adoption and positive experience of some of Datadog’s largest and most strategic customers. 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: Partner with Sales and Technical Solutions to proactively drive adoption from onboarding through to maturity. Own and project manage the on-boarding process for new customers. Discover, own and manage the customer’s success plan. Ensure consistent alignment with decision makers on the impact of their investment in Datadog. Proactively build and maintain targeted relationships with people of influence throughout the customer base. Collaborate cross-functionally with internal Datadog teams (sales, support, enablement, product, finance, and legal). Monitor and analyze usage trends to uncover potential renewal opportunities and risks. Act as the primary owner of customer adoption and health. With demonstrated understanding of observability and security platforms, align customers technical and business objectives to our platform offerings Who You Are: 3+ years in a Customer Success or Account Management role in B2B SaaS. Experience working with a portfolio of accounts that included large Enterprise. Experienced in driving discovery to understand your customers key pain points, objectives and priorities. Excellent in communicating complex concepts and aligning them to the pains and objectives of your customers. Experienced in shifting your message effectively to align with the expectations of the audience. You’re excited to work in a fast paced environment, where you’re expected to consistently develop your skill set and play an active role in the development of the team’s best practices. Able to t

The IT Go-to-Market (GTM) organization builds and operates the technology platforms that enable the end-to-end customer lifecycle, spanning Sales, Revenue Operations, Customer Support, Customer Success, and Professional Services. As a Senior Product Manager on the IT GTM team, you will own the product strategy and roadmap for post-sales technology platforms, including CRM, Service Cloud, support operations, customer portals, AI-enabled support capabilities, and enterprise integrations. You will partner with business and engineering leaders to define product vision, prioritize investments, and deliver scalable solutions that improve customer and agent experiences, drive operational efficiency, and support business growth. We are looking to speak to candidates who are based in Palo Alto, New York City, Seattle, or Austin for our hybrid working model. Responsibilities Domain Expertise Proven experience modernizing Technical Support platforms, including workforce planning, workflow automation and optimization, customer and partner collaboration and i, and knowledge-sharing solutions Strong understanding of end-to-end support user journey, including case creation and management, omni-channel case routing, prioritization, system integrations, and AI-driven proactive customer engagement Extensive knowledge of integration patterns across Salesforce Sales Cloud, Service Cloud, external support portals, and analytics platforms to deliver seamless customer and agent experiences Broad understanding of the post-sales ecosystem spanning Professional Services, Technical Support, and Customer Success, with the ability to design solutions that optimize cross-functional workflows Understanding of expertise in customer and support agent journeys, support processes, and operational workflows, with a track record of leading large-scale digital transformations and delivering custom solutions that address complex business challenges Expertise in building and scaling technology solutions f

The MongoDB Query Execution Team is hiring software engineers who want to join us in developing a high performing, reliable and modular distributed query system. Our engineers work on implementing and maintaining execution algorithms, building new query language features, tuning database performance, and more to power our customers' critical workloads. This role can be based out of our Dublin office or remotely in Ireland. Relocation can be supported. Position Expectations Understand and improve current functionality of the MongoDB query engine Contribute high quality C++ code and give and solicit feedback in code reviews Identify, design, implement, test, and support new features related to query performance and robustness, query language enhancements, diagnostics for query performance problems, and integration with other products and tools Work constructively with peers to deliver excellent technical solutions Candidate Profile 5+ years of experience in systems programming Experience in databases and/or data management systems is a huge plus, but not a requirement Hands-on experience building industrial-strength software Solid computer science fundamentals, with strong competencies in data structures, algorithms, and software design/architecture Experience with large code bases, preferably in C++, C, Rust or a similar compiled language B.Sc in Computer Science or similar field, or equivalent practical experience Interest in the theory and practice of database query engines. Hands-on experience or M.Sc./Ph.D in the domain is a plus Success Measures In three months you’ll have contributed to the development of a project slated for the next major version, as well as fixed a few bugs in a minor version of our latest stable release series In six months, you’ll have taken on code review responsibilities and are independently delivering complex functionality and squashing bugs independently In twelve months, you’re leading the development of a new major feature and are h

About Voyage AI Team at MongoDB Voyage AI team in MongoDB is building a best-in-class, general-purpose, domain-specific, and fine-tuned embedding models and rerankers to enable accurate, efficient unstructured data search and retrieval for RAG, recommendation, semantic search, and more. It is backed by a strong team of AI researchers from Stanford, MIT, Berkeley, Princeton, and CMU, who have conducted over five years of cutting-edge research on training embedding models. Voyage AI was acquired by MongoDB recently, and is now integrating the SOTA embedding models with MongoDB's data platform to create powerful end-to-end solutions. Position Overview We are seeking a Senior Research Scientist to join our team and contribute to the development of next-generation AI models. This position offers a unique opportunity to work on challenging problems at the intersection of machine learning research and practical deployment of large neural networks. We are looking to speak to candidates who are based in Palo Alto for our hybrid working model. Responsibilities Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers Innovate in next-generation information retrieval and LLM agent paradigm Collaborate closely with other research scientists and research engineers as well as peers across the organization Qualifications PhD degree in Computer Science or related field Strong background in machine learning, deep learning, and natural language processing Familiarity with training distributed training of neural networks for language and visual understanding What We Offer Opportunity to work on real-world problems at the cutting edge of AI research Opportunity to utilize research vision to innovate the entire company and make real-world impact Exposure to the full lifecycle of AI model development, from research to production Our compensation (base + equity) for this position is competitive with frontier AI labs About MongoDB

The Team This role can sit in our NYC HQ on a hybrid basis, or it can be fully remote while working from a location based in either Eastern or Central time zones. We are looking for an experienced Senior Engineer for our SRE, Atlas team to support, maintain and grow the Atlas platform. As a senior SRE, you will be expected to be able to design & build complex systems, operate with autonomy and act as owner for everything you do. The SRE Atlas team works alongside the various Atlas software engineering teams to provide expertise about running systems at scale, build new tooling and automation and perform essential maintenance of the Atlas fleet. This is an SRE team, which means you can expect a highly hands-on approach, tackling the technical challenges of implementing large scale solutions that have the ability to impact our customer’s most crucial workloads. Role Overview We are seeking a talented Site Reliability Engineer (SRE) with a strong infrastructure background. This role requires engineers to have a customer-first mindset to ensure that everything we do results in a stronger product and a better experience for all Atlas customers. The ideal candidate should Have 5+ years of experience running critical systems at scale Value efficiency in processes and operations, and display a preference for automation over manual processes (“allergic to ops work”) Be familiar with a major cloud provider (AWS, Azure, or GCP) and possess the ability to build and operate systems in a multi-cloud environment A strong understanding of how to run a large scale Linux environment, including low level fundamentals Firm grasp of at least one modern programming language, beyond basic scripting (Go, Ruby, Python) Solid understanding of web and network protocols and standards (HTTP, TLS, DNS, etc) Special Requirements: Be a US Citizen Expectations Participate in the development of a reliable and resilient multi-cloud platform that hosts business critic

With a strong security engineering background, you’re looking for a role that gives you the freedom to increase MongoDB’s resonance with customers by strengthening our core database products. You’re passionate about solving hard security engineering problems while putting a strong emphasis on customer experience, leveraging your own significant experience. You enjoy collaborating with different teams to innovate and implement pragmatic solutions. Who We Are The MongoDB Product Security organization is a diverse collection of individuals working together to scale MongoDB’s security, both security of the products themselves and the security features we offer to customers. The team is responsible for the MongoDB Database Server ( Community and Enterprise editions). The MongoDB Product Security organization works with software engineers to design, implement, and operate systems in a manner that protects customer data. It is a multidisciplinary team that covers product, software, cloud, infrastructure, and operational security concerns. The team does the following: Build a developer driven security program where there is tight integration with engineering artifacts, process, and tooling. Use software architecture and coding patterns to reduce the impact of security issues. Be security subject matter experts for our tech stack and products. We are looking to speak to candidates who are based in Dublin for our hybrid working model. Responsibilities You will take ownership, define strategy, and drive improvement for parts of our program such as fuzzing, threat modeling, secrets management, or container security Advocate for and lead complex security projects from inception through completion Drive architecture, patterns, and processes across Server Engineering that make security the easiest path Partner closely with engineering teams to design and implement security controls across our software and systems Research and POC new attacks against our systems. Plan and per

We are looking for an experienced Senior or Staff Engineer for our SRE, InfraSec team, to guide the security of our cloud-based infrastructure. As a Staff SRE, you will be very hands-on technically while also mentoring a small team of SREs. The InfraSec team collaborates closely with other engineering teams to ensure that our infrastructure adheres to the highest security standards. They build essential security infrastructure and implement controls that reinforce the platform’s security posture. This is an SRE team, which means you can expect a highly hands-on approach, tackling the technical challenges of implementing large scale solutions.This team is deeply involved in the technical aspects of security and the nuances of its actual implementation. This role can sit in our New York City, Austin, Seattle or San Francisco offices on a hybrid basis, or it can be fully remote while working from a location based in either Eastern or Central time zones. Responsibilities: Cloud Security Design and Implementation: Help lead the design and deployment of security solutions for cloud platforms (AWS, Azure, GCP), including network and compute security, identity management, and cloud security posture management (CSPM) Automation and Monitoring: Build automated solutions for real-time security monitoring, logging, and alerting in cloud environments. Leverage native cloud services and third-party tools for runtime security monitoring and anomaly detection Security Tooling: Evaluate, implement, and manage cloud-native security tools and platforms for endpoint security, identity management (IAM), and CSPM Qualifications: Experience: 6+ years of experience in SRE, infrastructure engineering or similar role, with a strong focus on security work, with ideally 2+ years in a senior or staff engineering role Security Mindset: A comprehensive understanding of all facets of cloud environment security, spanning from foundational OS networking laye