Technical Services Manager in New York

We are Datadog's in-house product experts. The Technical Solutions team enables Datadog's worldwide growth by educating potential clients and ensuring that existing customers are happy and successful. Premier Support Engineers (PSEs) are primarily focused on assisting prospects and customers with any technical questions about Datadog. PSEs engage with Datadog’s Premier Customers via standard technical support channels, but are also involved with cadence calls, demos/presentations, conferences, and various side projects. You will work directly with Datadog’s Premier Customer base, and will be immersed in a fast-paced environment where you will be challenged, but will also immediately witness your contributions to Datadog and to our 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: Respond to client requests (phone / chat / tickets) on our fast paced team while continuing to educate our clients on the use of the platform Develop relationships with our Premier Customers, working hand-in-hand to truly know their distinct environment Reproduce issues and dive into the 600+ integrations that Datadog works with Build out documentation and knowledge based articles for a variety of technology Drive product conversations based on needs and problems learned during client interactions Participate in routine health check meetings with Premier Customers Work from a Datadog office 3 - 5 days per week Who You Are: Experienced in multi-channel technical support at a SaaS company (2+ years of related experience) A tinkerer with some programming experience and a basic knowledge of Linux Self-motivated, detail-attentive, and have a desire for continuous learning A critical thinker who defaults to a client-centric approach A decision

About OpenAI OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. We build models and products that help people learn, create, and solve problems—and we work to do so safely and responsibly. About the Team OpenAI’s products are talked about by people, not just press and pundits. More than 900 million people use our tools each week, learning from one other and passing along what works. Their stories shape our reputation and encourage others to try our products. Our community team builds direct relationships & channels with people who use our tech then amplifies their stories and use cases so peers can learn from them and channels their insights to our product and research teams. About the Role We are seeking an exceptional Technical Community Program Manager to help manage and scale a high-signal community of advanced ChatGPT Pro users. This is a hands-on, technically fluent role at the intersection of community-building, product enablement, user research, product education, and editorial storytelling. You do not need to be a full-time software engineer, but you should be comfortable understanding technical workflows, asking sharp technical questions, using the latest AI tools, and helping advanced users explain what they are building. You will report to the Head of Pro Subscriber Community and will be based in New York City. In this role, you will: Run and grow a high-signal community Help manage and deepen engagement with the ChatGPT Pro cohort of advanced users across disciplines Design and execute high-touch community programming, including product demos, office hours, show-and-tell sessions, peer-learning formats, and in-person events Build repeatable systems for onboarding, engagement, retention, and member communications Build deep relationships with exceptional users Identify, recruit, and onboard new individuals doing high-impact work with ChatGPT and Codex Conduct in-depth interviews and maintain ongoing r

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 customers. You will own how they are built, shipped, and adopted. 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 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 management expertise, extreme ownership of outcomes, and an ability to immerse in customer workflows and partner with customer teams to solve complex engineering problems at pace. This role is based in NYC. We use a hy

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role: We’re looking for a Technical Enablement Manager to own the end-to-end learning, knowledge, and training experience for Notion’s global customer experience (CX) teams—spanning onboarding, change management, and continuous learning. This is an opportunity to build, lead and innovate on programs that ensure customer-facing support teams have the knowledge, skills and processes they need to consistently deliver high-quality support experiences to Notion customers. You’ll set the enablement strategy, partnering closely with CX leadership and cross-functional partners to identify skill, knowledge and process gaps, and translate those needs into durable enablement solutions—content, workflows, and programs. This role is based in New York City, USA. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days. What You'll Achieve: Own end-to-end CX enablement for a business vertical: define learning outcomes, readiness expectations, and content strategy across

The Technical Solutions (TS) Enablement team empowers Datadog's global post-sales organization—including Support, Customer Success, and Sales Engineering—with the skills, tools, and resources they need to deliver world-class customer outcomes. As a Senior TS Enablement Program Manager supporting Sales Engineering , you'll own the design and execution of complex, high-visibility enablement programs that drive lasting change across the TS organization. You'll operate with significant autonomy, lead cross-functional initiatives, and serve as a senior voice within the Enablement team—partnering closely with leadership to translate business priorities into measurable enablement impact at scale. 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 the end-to-end strategy and execution for complex, global enablement programs—managing multiple concurrent workstreams across regions, functions, and timelines with limited guidance. Lead cross-functional stakeholder relationships with senior partners across StratOps, Product, TS Leadership, and the People Team—negotiating scope, aligning on priorities, and driving accountability without positional authority. Translate business goals and OKRs into multi-quarter program roadmaps; communicate priorities, trade-offs, and progress to leadership with clarity and a clear point of view. Design and implement comprehensive metrics frameworks to measure change adoption and program effectiveness; pull and analyze Salesforce and LMS data to track performance against KPIs; present findings to senior stakeholders and iterate for lasting impact. Build and deliver executive-ready materials that tell a compelling business story—translating complex data, program updates, and regional insights into structured narratives with clear

TPMs at Datadog see the problems hiding between teams, engineer away the work that shouldn’t require humans, and drive the company’s most technically complex and consequential bets to completion. Technical Program Management at Datadog operates at the intersection of engineering depth and organizational reach by driving high priority, cross-functional programs that are too complex and consequential for any single team to own. We partner with engineering on solving deeply technical problems at scale by connecting the people, decisions, and context to move Datadog's most important work forward. We build the systems and automation that make entire classes of program work self-executing. We are in the architecture conversation early, earning trust through technical judgment. We use AI to surface risks earlier, accelerate program execution plans, and find cross-team patterns that would otherwise stay hidden. The faster teams move, the more essential it is to have someone who can operate across them. 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 We Expect: These are the expectations we hold for every TPM at Datadog. Technical depth, product domain expertise, and AI systems literacy; knowing how AI solutions work, where they fail, and the scope and impact of those failures. AI brings more complexity into the picture - the technical bar is higher, not lower. Build the systems that reduce the need for coordination Identify what matters before anyone asks, and automate the rest Engineer program lifecycles end-to-end See what no single team can see and own the solution Drive the company's most technically complex and consequential bets through cross-functional agreement, organizational visibility, and influence Build AI powered automation tools and

We’re looking for a Senior Technical Product Marketing Manager to join our security product marketing team and help bring Datadog’s rapidly growing security offerings to market. In this high-impact role, you’ll collaborate closely with Product, Sales, Sales Engineering, and Enablement to translate complex technical capabilities into compelling narratives that drive awareness, adoption, and differentiation. As the first hire in this space, you'll own key go-to-market efforts, lead technical positioning for strategic initiatives, and mentor others on content strategy and enablement best practices. This is a unique opportunity to shape how Datadog tells its security story to a global market. 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: Define and execute the technical marketing plan for Datadog’s security product line, from launches to scaled adoption. Partner cross-functionally with Product, PMM, Sales Engineering, and Enablement to craft differentiated messaging, inform roadmap decisions, and build cross-product solution narratives. Create and deliver high-impact sales tools including battlecards, investigation flows, objection handling guides, and competitive workshops. Lead competitive strategy by synthesizing market insights and producing content that positions Datadog as a differentiated leader in cloud-native security. Act as a technical subject matter expert and trusted advisor — coaching field teams, reviewing enablement content, and influencing internal strategy. Represent Datadog in customer briefings, industry events, and webinars, serving as a go-to voice on observability and security. Who You Are: 8+ years of experience in technical product marketing, developer relations, product management, solutions engineering, or related roles in

The GTM Enablement & Business Value Excellence team is the center of excellence responsible for how go-to-market execution is designed, enabled, measured, and continuously improved across Sales Enablement, Technical Solutions Enablement, and Business Value. We establish the capabilities, operating model, systems, analytics, and governance that enable our customer-facing teams to execute consistently at scale and adapt as our business evolves. Within the team, Enablement Operations designs and manages the operational infrastructure that powers enablement at scale. This includes operational governance, enablement systems, knowledge management, reporting, operational processes, and continuous improvement, ensuring programs can be delivered consistently, measured effectively, and continuously optimized. The Opportunity: As an Enablement Operations Manager, you will own the operational infrastructure that enables our programs to run efficiently and consistently at scale, with a primary focus on supporting Technical Solutions Enablement. You will partner closely with the Technical Solutions Enablement team to manage operational processes, enablement systems, knowledge management, reporting, governance, and continuous improvement. You will also collaborate with the Enablement Systems & Analytics team and cross-functional stakeholders to evolve the operational capabilities that support scalable execution. This role is ideal for someone who enjoys building scalable systems and processes, improving how work gets done, and creating the operational foundation that enables high-performing teams. 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: Support the operational processes, governance, and enablement systems that support Technical Solutions Enablement. Man

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 strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Requirements: 5+ years of experience writing high-quality production code Experience building high-performance distributed systems at a large scale (the more battle scars, the better) Strong cloud skills Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.) Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!) Ability to work in-person in our NYC or SF office. Prior experience with Rust is nice to have, but not required. Ability to participate in on-call rotation and respond to production incidents.

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 strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

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 strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity. Requirements: 5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software. Knowledge of advanced Python features, especially async programming. A strong product sense that manifests as a focus on developer ergonomics and productivity. A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems. Ability to participate in on-call rotation and respond to production incidents. Ability to work in-person in our NYC or Stockholm office. Any of the following would be a plus:

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 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

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Our team is a fast-growing group of researchers and engineers focused on building reliable ML systems and pushing the boundaries of LLM inference efficiency. We develop techniques that improve how models execute in production, driving lower latency, higher throughput, and consistent quality across diverse workloads. As an engineer on this team, you’ll work across the inference stack to improve core performance metrics by diving deep into model execution, identifying bottlenecks, and developing innovative optimizations. You’ll collaborate closely with modeling and systems teams to experiment, measure, and ship improvements that meaningfully accelerate inference. As the team evolves, you’ll have opportunities to build expertise in advanced performance techniques, including GPU/CUDA optimizations, kernel-level improvements, and model execution strategies for MoE and large-scale architectures. Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, e