Eng 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 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

Datadog is seeking curious, driven interns to join our Product Management team and help build products that improve how engineers monitor and understand their systems. As a Product Management Intern, you'll support the product development lifecycle by partnering closely with Engineering, Design, and Product Marketing to bring new ideas and features to life. You'll gain hands-on experience working on products that serve highly technical customers while contributing to meaningful business and user outcomes. Interns are embedded directly within product teams, working on meaningful initiatives alongside full-time Product Managers and contributing to actual product decisions. Our platform processes over 100 trillion events per day across 30,000+ customers in a multi-cloud environment -- giving you direct exposure to large-scale, real-time systems built by engineers, for engineers. It's an environment where you'll develop product thinking, technical communication, and cross-functional collaboration skills by doing the work, not just observing it. 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: Conduct customer discovery conversations and gather feedback to better understand user needs Drive product initiatives from concept through launch alongside Engineering, Design, and Product Marketing teams Translate customer and business needs into clear product requirements and engineering priorities Analyze customer feedback, product data, and market insights to help inform product decisions Prepare and deliver technical product demonstrations and communication materials Develop technical understanding of Datadog’s observability platform and cloud infrastructure products Who You Are: Targeting a 2028 full-time graduation or start date Pursuing a degree in

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are a visionary and innovative industry expert team with a track record of architecting, building, and taking solutions to market applicable to various sub-verticals across financial services, spanning retail and commercial banking, lending and payments. There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn't stop there. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Are you passionate about taking new ideas and innovation to market? Have you successfully applied technology to solve business problems in core banking? If so, we have a strategic opportunity to use your expertise across Data, Cloud, and AI to drive transformational changes across the financial services industry in a highly visible role collaborating with internal and external leadership. Snowflake is seeking a seasoned Financial Services Industry Architect with deep, hands-on experience designing and d

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 performa

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. About the Role 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. 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 deploy them at every stage of program execution What you will do See Across: Identify What No Single Team Can See Own large cross-functional programs spanning multiple engineering orgs, product, and business functions Proactively surface systemic risks, cross

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

The Dashboards product is Datadog's unified single-pane-of-glass for metrics, logs, and traces—a comprehensive treasure trove of observability data. We are transforming Dashboards into an AI-native control surface and the central hub where every team moves seamlessly from question to insight to action – providing a guided experience that feels like having an expert SRE at your side and ensuring the entry point is never an empty canvas. We're hiring a Staff Applied Scientist to define and guarantee the quality of this AI system at scale. "Good" isn't one number — it spans answer quality, tool-selection accuracy (critical given the growing catalog of data sources and visualizations), retrieval relevance, latency, token cost, and end-to-end agent success. The space is full of open questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when a user’s query can result in the agent making decisions against dozens of visualizations and data sources – both of which are growing month over month? How do you build a measurement system that catches regressions across all widget types and data sources (e.g., enforcing correct grouping, sorting, and time overrides), and is easy to use and extend by dozens of teams? If those are the problems you want to spend your time on, come build this with us. 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 evaluation strategy for Dashboards, as well as sister teams within our organization. Define the metrics — offline and online, quality and cost, single-turn and multi-turn — that the team and the broader organization optimize against. Build the eval datasets, golden traces, and regression harnesses that catch quality changes before they hit customers, an

Team description At Datadog, AI agents are becoming first-class consumers of observability, security, and software delivery data — from third-party coding agents like Claude Code, Cursor, and Copilot, to our own Bits SRE, Bits Assistant, and Bits Dev Agent. The Agentic Interfaces team owns the platform that connects these agents to Datadog: the MCP Server, the tools and retrieval surfaces agents call into, and — critically — the evaluation systems that tell us whether an agent's experience on Datadog data is actually getting better over time. This role is about that last piece. We're hiring a Staff Applied Scientist to define what "good" means for an Agentic interface at Datadog and to build the measurement systems that make it true. "Good" isn't one number — it spans answer quality, tool-selection accuracy, retrieval relevance, latency, token cost, and end-to-end agent success on real customer workflows. You'll design the evals, build the datasets, define the metrics, and partner with the AI engineers on the team to land the platform that lets every product group at Datadog ship integrations that are demonstrably better release over release. The space is full of open research questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when the tool catalog has hundreds of entries and grows weekly? How do you build a measurement system that catches regressions across first-party and third-party agents at once, without each team writing their own harness? If those are the problems you want to spend your time on, come build this with us. 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 skills, we encourage you to apply. What You’ll Do: Own the evaluation strategy for Datadog's AI agent integrations. Define the metrics — offline and online, quali

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 the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the Role 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 focus on the Financial Services vertical, partnering with banks, asset managers, and private capital investors to deploy next-generation AI capabilities across their operations, investment processes, and portfolio companies. You will own delivery end-to-end: embedding with Financial Services 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 New York City. We use a hybrid work model of 3 days in the office per w

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva, Singapore and São Paulo. The Role The Client Success Management (CSM) team at Addepar is responsible for the complete post-sales lifecycle of a client. Through strategic guidance and partnership, CSM ensures clients derive maximum value from the platform, leading to overall adoption success, retention, and renewal. Few roles provide such a direct impact on the growth of the company. The CSM Team manages some of Addepar’s largest and most sophisticated clients, including Single Family Offices, Multi-Family Offices and traditional Wealth Management Firms. You will become a trusted partner, deeply understanding and advising on their business, including their strategy, clients, services, team, and technology, and helping them increase the value they get from Addepar. This role works closely with the Account Executive, cross-functional R&D, and Services teams to effect change management and drive standard methodologies and utilization of the platform. You will be the client’s internal advocate, helping ensure an excellent experience, and capturing feedback on ways that Addepar can build the next generation of client-centric solutions. Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa spons

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva, Singapore and São Paulo. The Role The Client Success Management (CSM) team at Addepar is responsible for the complete post-sales lifecycle of a client. Through strategic guidance and partnership, CSM ensures clients derive maximum value from the platform, leading to overall adoption success, retention, and renewal. Few roles provide such a direct impact on the growth of the company. The CSM Team manages some of Addepar’s largest and most sophisticated clients, including Single Family Offices, Multi-Family Offices and traditional Wealth Management Firms. You will become a trusted partner, deeply understanding and advising on their business, including their strategy, clients, services, team, and technology, and helping them increase the value they get from Addepar. This role works closely with the Account Executive, cross-functional R&D, and Services teams to effect change management and drive standard methodologies and utilization of the platform. You will be the client’s internal advocate, helping ensure an excellent experience, and capturing feedback on ways that Addepar can build the next generation of client-centric solutions. Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa spons

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