Engineering Lead Analyst in New York

Reports to: Head of Developer Growth · New York, NY NOTE: This is not a traditional marketing role, it's a growth role that reports into the Product org. We're hiring someone to build our developer audience the way the best consumer brands build audiences, with a real voice, a point of view, and the speed to act on both. You'll own where and how we show up in the places developers actually spend time, run our developer social channels, expand into new ones, and build the tooling and measurement behind it. This is a builder role, not simply a coordination role. What You'll Do: Own the voice, editorial standard, and publishing cadence for Datadog's developer channels (@datadogdevs on X, Datadog Developers on LinkedIn) Treat replies as a primary surface. The best accounts win in the mentions, not the feed Expand into the places developers actually spend time: Reddit, YouTube, Discord, Bluesky, dev.to, Hacker News, and whatever you make the case for. Always openly as Datadog — developers catch anything else immediately, and you only get caught once Turn shipped engineering work into things people want to read and watch. Releases, changelogs, war stories, the occasional postmortem that deserves a wider audience Build the attribution model connecting what you publish to trials and signups. Nobody is handing you this one Run real experiments: a hypothesis, a readout, and the willingness to kill something publicly when it doesn't work Lead the developer content engine around events Get other people posting — Datadog engineers under their own names, and developers outside the company who already like us and could use the support Track what developers are saying about us and our competitors, and route it back to product and growth Who You Are: You've grown an audience from a small number to a large one You can point to outcomes your content moved, not just reach You're a strong writer with a distinctive voice, and you know the difference between writing about an audience and

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. Role Overview At Snowflake, we are powering the era of the agentic enterprise. We are looking for AI-native builders and operators who use AI as a core part of how they think, learn, and execute. The Startup Business Development Lead will be a key member of the Snowflake for Startups team, responsible for identifying, engaging, and growing relationships with the most strategic AI-native startups. This person will work across Sales, Product, Marketing, AI Partnerships, Ventures, and Solutions Engineering to help high-potential startups build with Snowflake and accelerate adoption of Snowflake AI. This role requires a rare combination of startup ecosystem credibility, business development judgment, and practical understanding of modern AI application architectures. The ideal candidate can engage founders on both business value and technical direction, and can translate Snowflake’s AI platform into compelling growth opportunities for startup partners. This is an individual contributor role and will be accountable for building a high-quality pipeline of AI-native startup opportunities, increasing activation and adoption of Snowflake AI products, and driving long-term consumption growth from strategic startup partners. Key Responsibilities Build and grow relationships with high-

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 hiring a Compute Strategy and Operations lead to own how Modal plans for and acquires GPU and CPU capacity. You'll size our infrastructure needs ahead of demand, source supply across hyperscalers, neoclouds, and datacenter operators, and negotiate and close the contracts to secure it. The compute you secure directly determines what Modal can sell and build. In this role, you will: Own end-to-end procurement of GPU and CPU capacity across hyperscalers, neoclouds, and datacenter operators Build and maintain a strong pipeline of supplier relationships Evaluate supply options on price, availability, hardware specs, networking capabilities, and SLA terms Negotiate and close contracts: reserved capacity agreements, spot arrangements, MSAs, DPAs, and order forms Work closely with our engineering teams to translate technical requirements into procurement specs Track

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Security Engineering is the engineering function inside the Plaid security org that focuses on developing the industry-leading security systems and infrastructure. Security Engineering owns most of Plaid’s security-related infrastructure: secure data storage, key management systems, internal identity platform, internal authentication systems, internal permission management, and internal authorization service. We develop solutions across data encryption, key management, access control, and data loss prevention to protect sensitive consumer data. We believe in the Zero Trust security model and are always looking for ways to improve our authentication and access control platforms. About the role: You will develop security capabilities to secure Plaid infrastructure and sensitive data access. You will lead the team’s strategic planning in collaboration with the manager and other senior engineers. You will own, maintain, and build Plaid’s security infrastructure and services like IAM Gateway, Key Management System and Network Firewall. You will consult with product engineers to ensure Plaid services meet security standards. You will help educate and support other engineering teams to improve security in

Code Intelligence sits at the intersection of agentic engineering and product, running agents against real customer source code in production. This is a chance to own an AI-native team at the center of a business-critical growth area. High visibility, high leverage, and squarely in the fastest-growing part of the AI agent space. 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 employees can create a work-life harmony that best fits them. What You'll Do: Lead and develop an existing engineering team, building trust, setting technical direction, and establishing a high bar for ownership and execution. Own the roadmap and execution across four areas: source code indexing, PR Agentic Reviewers (agents that generate artifacts like metric descriptions on customer pull requests), shift-left work (extending code-writing agents to make direct changes to customer repos), and closed-loop feedback and evaluation systems including LLM-as-judge. Set the team's product strategy independently while partnering day-to-day with product teams company-wide — this team functions as its own product org. Build and operate scaled LLM and agent software in production: LLM APIs, agent frameworks, harness and tool-use layers, prompt and eval tooling, and closed-loop evaluation systems that measure and improve agent output quality. Partner closely with customers and internal stakeholders to deeply understand needs and translate them into technical direction. Drive the team's AI-native development practices, setting high standards for safety, validation, and increasing agent autonomy over time. Who You Are: Experienced engineering manager with a track record of shipping products with direct customer interaction, not just internal stakeholders. Hands-on experience building scaled LLM software: LLM APIs, agent frameworks, prompt and eval tooling, h

About the Team The Technical Success team helps OpenAI’s customers realize meaningful and sustained value from our technology. We partner with customers throughout their journey—from initial exploration and solution design to production implementation and organization-wide adoption. Applied AI Engineers serve as trusted technical partners to customer executives, engineering teams, product leaders, security organizations, and transformation teams. They combine deep technical judgment with strong customer instincts, translating frontier AI capabilities into secure, reliable systems and durable business outcomes. About the Role We are seeking a Manager to build, lead, and develop a high-performing team of Applied AI Engineers supporting our enterprise customers. You will be accountable for the technical success of a broad and strategically important customer portfolio. You will help your team identify high-value opportunities, design and deploy production-grade AI systems, navigate complex technical and organizational constraints, and expand successful implementations across workflows, teams, and business units. This role requires technical depth, people leadership, customer judgment, and operational rigor. You should be comfortable coaching engineers through architecture and evaluation decisions, engaging directly in high-stakes customer situations, and collaborating with Sales, Solutions Engineering, Product, Research, Engineering, Security, and Legal. You will also help define how we serve enterprise customers at scale by developing effective coverage models, reusable implementation patterns, technical enablement, escalation mechanisms, and systems for turning field insights into high-quality product feedback. In This Role, You Will Build, manage, and develop a high-performing team of Applied AI Engineers supporting large and complex enterprise customers. Own the quality and impact of the team’s work across solution design, implementation, production readiness, adop

Applied AI is where Datadog's ambitious AI bets get built and shipped ( Bits Chat , updog ). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds the foundations for agentic systems capable of operating at scale in complex production environments. Current bets span agents that run autonomously at scale, context and memory layers that make those agents more intelligent over time, and tools that help customers build and validate AI-native services in production. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth. As an Engineering Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter. At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Lead and develop a team of engineers and applied scientists focused on building the foundations for agents operating at scale Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality Navigate the unique challenges of shipping AI-powered products: balancing quali

Datadog's Application Performance Monitoring (APM) provides deep visibility into the health, performance, and lifecycle of modern distributed applications, tracing requests from end-user devices (web and mobile) through to backend services. Our goal is to help customers detect root causes faster, optimize application performance, and improve resource efficiency at scale. As the Engineering Manager for APM Serverless, you will help define and deliver the end-to-end serverless APM experience, from auto-instrumentation through troubleshooting, and ensure that OpenTelemetry and Datadog-native customers alike have a frictionless and performant journey. You will also lead efforts to expand coverage of cloud-managed services across providers, ensuring customers can seamlessly trace and monitor critical services in all major and emerging cloud environments. We’re looking for an experienced engineering leader who thrives at the intersection of infrastructure and developer experience. You should care about well-designed APIs, observability-first thinking, and building systems that empower other developers. This is a high-leverage role that will influence how developers across the industry understand and instrument their serverless workloads. 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: Lead a polyglot team of 8-9 engineers and partner closely with Product and Engineering teams across Datadog to deliver industry-leading serverless capabilities that power consistent, scalable, and intuitive instrumentation across languages. Drive a domain that is technically rich: Lambda, Azure Functions, GCP, OTel billing, Rust, durable functions, distributed tracing across managed services. Engineers on this team work

The Detection Platform organization is responsible for helping customers identify, understand, and act on issues across their environments through alerting, event intelligence, and autonomous detection capabilities. As Director, Detection Platform, you will lead a group of engineering managers and teams responsible for foundational alerting infrastructure, event management, monitor creation experiences, and AI-powered detection systems. This role sits at the center of Datadog’s efforts to evolve how customers detect, investigate, and respond to operational issues at massive scale. You will partner closely with Product Management, Applied Science, Design, and Engineering leaders to shape the future of detection and observability experiences for Datadog customers while leading a growing organization of engineers. 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: Lead a multi-team engineering organization responsible for alerting, event management, monitor creation experiences, and autonomous detection capabilities. Define and execute the technical and organizational strategy for the Detection Platform while aligning stakeholders across Engineering, Product, Design, and Applied Science. Drive innovation in AI-powered detection, anomaly identification, and signal generation that helps customers proactively identify and resolve issues. Scale highly available platform systems that process hundreds of millions of evaluations while maintaining reliability, performance, and operational excellence. Develop and mentor engineering managers and technical leaders, fostering a culture of execution, collaboration, and technical rigor. Champion customer-centric product thinking by balancing platform investments with intuitive user experiences and measurable customer

Manager I, Engineering - Change Experience Platform The Change Experience Platform team builds the internal experiences and platform capabilities that help Datadogs understand, author, route, and safely manage infrastructure changes. The team owns internal UI and CLI frameworks, change-management user experiences, notification and subscription platforms, and infrastructure governance signals used across Datadog’s engineering organization. Its work sits at the intersection of developer experience, infrastructure operations, product design, and change safety. We’re looking for a hands-on technical leader to manage and grow a team of engineers working on the systems that shape how Datadog engineers interact with infrastructure change. You will partner closely with infrastructure, developer experience, platform engineering, and product teams to build reusable interfaces, workflows, and safety mechanisms that make complex change processes easier to understand and safer to execute. This is a high-impact role for someone who enjoys combining product thinking with strong engineering judgment. You will help the team balance framework ownership, platform reliability, internal customer needs, and long-term technical direction across a portfolio that includes UI systems, CLI authoring and publishing, change-management workflows, notification routing, subscriptions, and infrastructure cordon management. 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: Lead and grow a small team of engineers responsible for internal platforms and product experiences used across Datadog engineering. Help define what “good” looks like for internal developer-facing platforms, including usability, reliability, documentation, adoption, and supportability. Se

Role Overview You’re a seasoned Site Reliability Engineer who loves owning complex infrastructure, making things run faster, safer, and with less manual effort. In this Staff‑level role, you’ll design and operate VMware‑based private cloud platforms that power mission‑critical SaaS products used by customers around the world. You’ll work across Linux, Windows Server, networking, storage, and automation frameworks to increase reliability, reduce toil, and modernize a global datacenter environment. You’ll have the scope to set technical direction, build automation at scale, and mentor engineers while staying hands‑on with VMware vSphere, F5/AVI load balancers, and hybrid Active Directory. Here’s a breakdown of what you’ll do (not all of it, just the important stuff) Lead the architecture, deployment, and ongoing optimization of VMware vSphere–based private cloud infrastructure across multiple global datacenters. Design and build automation using PowerShell/PowerCLI, Ansible, Python, and CI/CD tools to streamline provisioning, configuration, and compliance. Administer, harden, and troubleshoot Linux (RHEL/CentOS/Ubuntu) and Windows Server environments that host enterprise and SaaS workloads. Integrate and manage Active Directory for authentication, access control, and service accounts across hybrid on‑prem and cloud environments. Partner with network and security teams to manage firewalls, VPNs, storage, and load balancers (F5 BIG‑IP, AVI/NSX Advanced Load Balancer) for highly available services. Document architectures and runbooks, participate in on‑call and change management, and mentor engineers while influencing long‑term reliability and automation strategy. These are the essentials you’ll need to get an interview 10+ years of experience in systems or infrastructure engineering, including operating large‑scale enterprise or SaaS datacenter environments. Deep hands‑on expertise with VMware vSphere (ESXi, vCenter, DRS, HA, vMotion, distributed switches) in production

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 Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside our most strategic customers. You will own discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams. You will measure success through production adoption, measurable workflow impact, and eval-driven feedback that changes product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM teams. This role is based in New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role you will Own technical delivery across multiple deployments from first prototype to stable production Build full-stack systems that deliver customer value and sharpen how we learn Embed closely with customer teams, understand their needs, and guide adoption of what you build Scope work, sequence delivery, and remove blockers early Make trade-offs between scope, speed, and quality; adjust plans to protect delivery Contribute directly in the code when progress or clarity depends on it Codify working patterns into tools, playbooks, or building blocks that others can use Share field feedback that helps Research and Product understand where the models succeed and where they can improve Keep teams moving through clarity and follow-through You might thrive in this role if you Bring 5+ years of engineering or technical deployment experience that includes customer-facing work Have scoped and delivered complex systems in fast-moving or ambiguous environments Write and review production-grade code across frontend and backend using Python, JavaScript, or c

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 Forward Deployed Engineers on our engineering team who want to work at the intersection of deep infrastructure work and direct customer impact. As an FDE, you'll partner with leading AI companies and foundation labs on cloud architecture, networking, storage, containerization, sandboxing, and more — helping them design and ship production infrastructure on Modal's platform. The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the infrastructure stack, and energy for working directly with customers on hard problems. You will: Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and deploy massive-scale production workloads on Modal Lead technical discovery and architect

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

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 We are seeking a Staff Full Stack Software Engineer to join the Advisor Experience team as our Technical Lead. Our team is focused on building tools for financial advisors to grow and sustain their business. We oversee bespoke products for advisors and develop advisor-focused capabilities throughout the Addepar platform. In this role, you will be the primary technical anchor for new capabilities including Secure Message Center — a compliant messaging experience built into Addepar's client portal that allows advisors and their clients to communicate directly within the platform. You will partner directly with Engineering Leadership and Product Management to build a modern, scalable architecture from the ground up. Beyond system design, you will act as a true engineering multiplier: setting technical standards, mentoring junior and mid-level engineers, and working alongside other senior engineers and AI specialists to deliver high-impact advisor tools. Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa sponsorship (for example, employment-based visas such as H-1B, F-1/OPT, or similar), and must be authorized to begin work in the U.S. on their first day of employme