Sourcer in New York

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy. What You'll Do: Platform & Runtime Security Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments) Strengthen boundaries between customers, workloads, and internal systems Identify and mitigate risks in distributed, dynamic compute environments Container &

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for engineers with deep AI/ML and low-level systems experience who want to build the best technical support experience in the world. This isn't a traditional support role — it's an engineering role where you happen to be closest to our customers. You'll split your time roughly 50/50 between working directly with customers and shipping fixes, features, and automation that improve Modal for everyone. When you help a customer debug a training run, you'll also fix the underlying issue in the platform. When you notice ten customers hitting the same friction point, you'll build the tooling or automation that eliminates it entirely. This role is for people who solve problems, not people who answer tickets. The problems you encounter are deeply technical and arise from running some of the most demanding AI workloads in the world. You'll be a member of our eng

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a People Operations Generalist to join our growing People team. You'll touch the employee lifecycle end-to-end — from offer acceptance through offboarding — while helping to build the processes and documentation that let our People function scale with the business. This is a great fit for a highly organized, systems-oriented people person who thrives in a fast-paced environment and wants to build operational foundations, not just maintain them. What you’ll do Own and continuously improve the new hire onboarding experience, ensuring employees are set up for success and internal tasks are tracked and completed on time. Serve as a first point of contact for employee questions across the full HR spectrum, triaging and routing more complex issues to the right People team member or external partner. Maintain and improve self-service resources (FAQs, Not

AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. About the Role: As a Manager, Enterprise Sales, you will lead and scale our enterprise sales team, driving strategic revenue growth with a consultative, customer-first approach. You will oversee complex deal cycles, coach Enterprise Account Executives, and build the motion that wins high-impact, multi-stakeholder deals in a rapidly evolving AI landscape. What You’ll Do Lead, mentor, and develop a team of Enterprise Account Executives, fostering a culture of performance, strategic thinking, and collaboration Own and guide the full enterprise sales cycle, from targeted outbound and discovery to multi-threaded navigation, negotiation, and close Build and refine enterprise sales playbooks, qualification frameworks, and forecasting models that increase accuracy and velocity Collaborate cross-functionally with Product, Marketing, and Engineering to align on go-to-market strategy, unblock en

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiari

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a Growth Engineer to own the technical foundation of Modal's marketing and developer-facing web surfaces: the marketing site, docs site, growth landing pages, high-profile microsites, forms, analytics instrumentation, and the integrations that help users discover, understand, and get started with Modal. This is a frontend-heavy role for someone with strong product taste, web engineering craft, and a business-owner mindset. You'll partner with Product Engineering, Design, Data, and Growth to ship polished, measurable web experiences from high-profile projects like the GPU Glossary and LLM Engine Advisor to internal tooling that helps teams publish content faster. When this role is going well, Modal launches new pages, docs experiences, campaigns, and experiments quickly without sacrificing performance, craft, or measurement. In this role you will:

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell. You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run. What you'll do: Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spik

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a Detection & Response Engineer to build the systems that help us identify, investigate, and respond to threats across our platform. This is an engineering role focused on automation. You'll build detections, investigation tooling, and response capabilities that scale with our infrastructure, using AI where it meaningfully improves signal, investigation speed, and operational effectiveness. You'll work closely with infrastructure, platform, and security engineers to ensure every incident makes the platform more resilient. What You'll Work On: Detection Engineering Design and build high-fidelity detections for attacks, abuse, and anomalous behavior across our infrastructure and production systems Continuously improve detections based on telemetry, threat intelligence, and lessons learned from incidents Improve visibility across cloud infrastruc

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What you'll do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustnes

About the role: Datadog is building a product-led Competitive Intelligence function to help inform product strategy, roadmap prioritization, positioning, and competitive readiness. As the Head of Competitive Intelligence you will build and lead the CompIntel function across priority competitors and adjacent market opportunities. You will translate competitor product evolution, technical capabilities, launches, pricing value, customer sentiment, analyst narratives, and ecosystem shifts into actionable insights for Product, GTM, Sales Engineering, Customer Success, Technical Advocacy, and leadership. In this highly technical, hands-on leadership role, you should understand how observability products are implemented, where workflows differ, what technical claims are credible, and how product gaps translate into customer impact. What You'll Do: Own the recurring competitive radar for Datadog’s priority competitor set, including taxonomy, source standards, evidence quality, and update cadence. Produce competitive deep dives, feature-gap analyses, early-warning briefs, and opportunity assessments tied to specific product domains and leadership questions. Translate CompIntel findings into roadmap recommendations, product differentiation hypotheses, and Product-facing decision guidance. Build and maintain a central CompIntel repository with competitor profiles, source notes, comparison frameworks, evidence logs, and decision-ready briefs. Convert validated findings into GTM-ready handoffs for battlecards, objection handling, strategic deal narratives, and field enablement. Synthesize signals from Product, Engineering, Sales Engineering, Customer Success, field teams, technical communities, Advocacy programs, win/loss, customer feedback, analyst research, public competitor materials, and CABs. Partner with Product Management to frame competitive questions, identify decision criteria, and connect findings to roadmap and prioritization discussions. Partner with Tec

We're looking for a Staff Engineer to join the Logs organization at Datadog and help redefine how our customers ingest, query, and derive insights from logs data. In this role, you’ll work closely with Product Managers and customers to drive complex initiatives across ingestion pipelines, search infrastructure, and intelligent log management capabilities - all while pushing the boundaries of what’s possible with AI and distributed systems. You’ll have the opportunity to lead efforts that shape the future of log management. 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 Product Managers to define ambiguous product requirements and determine the most impactful solutions for customers Lead technical strategy and execution and design systems surrounding log query performance and ingestion at scale. Explore and prototype new capabilities and collaborate with peers on initiatives spanning AI-powered log management, security and business operations, advanced query capabilities, and external data sources query capabilities. Mentor engineers across levels and contribute to growing a high-performing, collaborative team culture Who You Are: You have deep experience architecting and scaling backend systems, with a strong focus on data-intensive or distributed infrastructure You excel in ambiguous environments, demonstrating a mix of drive, curiosity and pragmatic decision-making You’ve partnered effectively with Product Managers and customers to define product direction and ship impactful features You have expertise in debugging complex systems and optimizing performance across real-time data pipelines You have experience in using AI agents tools in your day-to-day engineering practices You lead by example and enjoy helping others grow through m

We're looking for an Engineering Manager II to own and grow the Observability Pipelines engineering org at a pivotal moment in the product's lifecycle. Observability Pipelines is Datadog's on-premise, vendor-agnostic telemetry pipeline product, with a lot still to build as it grows and scales. It sits at the center of a fast-consolidating market, is central to Datadog's data pipeline optimization story for Logs and Metrics customers. This is a build-and-scale opportunity: you'll grow the management and technical leadership layers, co-own the roadmap with Product, and define how this org operates as it continues to expand. 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: Directly manage the OP org including EM1s across NYC and Paris, set technical direction, and be the connective tissue across a distributed team Build out the management and technical leadership layers as the org continues to grow - today ~20 ICs Partner directly with Product to co-own the roadmap and strategy, helping decide where OP’s engineering investment goes next Set and evolve the operating rhythm across the group: planning cadence, on-call and incident standards, and cross-team alignment Own key cross-org relationships with the SaaS Logs Pipelines team, the BYOC team, and the Vector open-source community Coach managers and senior engineers, and build the succession and growth plans that let the org scale beyond you Who You Are: Experienced managing managers across distributed teams, with a track record of raising the bar on how those teams operate, not just delivering through them Back

The product operations team enables product excellence at scale at Datadog. With over thirty standalone products on a single platform, product operations drives consistency, efficiency, and quality across the organization. As a Product Operations Manager, you will help the product organization do its best work. You'll build and scale the processes, frameworks, and cross-functional workflows that enable PMs and their partners to ship effectively. This includes touchpoints with inbound, delivery, outbound workstreams in product development. This is a role for someone who thrives in the space between teams: driving alignment, removing friction, and ensuring that the operational foundations of the product org can keep pace with Datadog's growth. 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: Own and improve the full Product Development Life Cycle . Own and drive operational excellence for product launches. This includes coordinating across Product, Engineering, Marketing, Sales, Customer Success, and Support to ensure we are ready to bring quality products and support to our customers. Build AI-powered workflows and agents that automate and accelerate operational work. From customer feedback triage to launch tracking to cross-functional reporting, you'll experiment with and deploy AI tools and custom agents to help the product org move faster and smarter. Build and maintain a single source of truth for launches giving cross-functional partners real-time visibility into what's shipping, when, and what's needed from each team. Drive centralized product knowledge and enablement , ensuring PMs and stakeholders have the tools, resources, templates, and context they need to succeed and making it easily discoverable. Analyze and quantify product signals. Query pro

Developers are shipping more code than ever, accelerated by AI-assisted development and increasingly complex software delivery workflows. As a Product Manager II on the Developer Engagement team, you'll help bring Datadog's insights and automation directly into the tools developers use every day, making software delivery faster, safer, and more efficient. You'll define customer-facing experiences that connect Datadog's observability, CI/CD, testing, security, and AI capabilities with pull requests, code reviews, and developer workflows. This role offers the opportunity to work closely with customers, engineering, design, and go-to-market teams while shaping the future of software delivery for developers. 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 customers, developers, platform engineering teams, and internal stakeholders to understand software delivery challenges and identify opportunities to improve developer workflows. Own the product strategy and roadmap for developer-facing experiences within pull requests, code reviews, and source code management platforms. Collaborate across Software Delivery products to surface actionable insights from CI/CD Optimization, Test Optimization, Code Coverage, Code Security, Deployment Gates, Bits AI, and future capabilities. Work across source code ecosystems including GitHub, GitLab, Azure DevOps, Bitbucket, and enterprise environments to deliver scalable integrations. Partner with engineering teams to connect Datadog signals with recommendations, AI-assisted workflows, generated fixes, and future automated remediation experiences. Measure product success using customer adoption, engagement, feedback, and business outcomes while partnering with Sales, Product Marketing, Solutions Engineering, Custo

The Internal Product Analytics (IPA) team is the analytics backbone of Datadog's Product organization. With over thirty products on a single platform, IPA gives PMs and leadership the data, tooling, platform, and analysis they need to make good decisions. The team owns the recurring analytical work the Product org runs on and builds the AI-first workflows that make that analysis faster and more consistent across the org. As Manager of the Internal Product Analytics team, you will directly manage a team of data analysts, partner closely with the PMs your team serves, and partner with the associated platform engineering teams. You will set the direction for how the team delivers analysis, builds AI-first tooling, and partners across functions. This is a role for someone who works well across teams, turning complex data and open questions into clear, trusted answers that PMs and leadership can act on. What You'll Do: Guide and grow the Internal Product Analytics team. Manage, coach, and develop a team of data analysts. Set priorities, hold a high bar for quality, and make sure the team's output is trusted across the Product org. Partner directly with the PM org. Work side by side with the PMs your team serves to frame the questions that matter, shape the analysis, and make sure the answers reach them in a form they can act on. Own the recurring analytics the PM org runs on. Business reviews, feature request analysis, usage and adoption tracking, and pricing analysis. Make this work consistent, repeatable, and fast so PMs get answers when they need them. Build AI-first analytics. Design and ship AI-powered workflows and agents that do the heavy lifting of analysis, from data querying to synthesis to reporting. Set the standard for how the team uses AI so analysis scales without simply adding headcount. Partner across functions. Work with Finance, Data Platform, Engineering, and Revenue teams to align on definitions, source the right data, and turn raw signals into decis