Infrastructure Engineer 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: Modal considers high-quality documentation to be essential for developer experience, and we see docs becoming even more important as agents increasingly deploy and operate Modal Apps. We are looking for a content-minded engineer who will partner with our product teams to curate Modal’s technical documentation and maintain a high quality bar across multiple dimensions. Responsibilities: Thinking holistically about content architecture and how the docs should evolve as Modal introduces new products and features Innovating on novel documentation formats and delivery channels to optimize agent productivity, in collaboration with our Agent DX research team Developing content standards, style guides, and automated enforcement mechanisms to ensure consistent style and high quality Building and maintaining automated pipelines that will enforce the correctness of code examp

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 an Engineering Manager to lead a team of highly experienced engineers building the infrastructure that powers Modal's serverless GPU platform. This is a hands-on leadership role — expect to split your time between technical contribution and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, large-scale data handling, and performance optimization. Who You Are You're an experienced engineering leader who stays close to the work and builds alongside your team when it counts. You earn trust through technical depth, not title. You communicate clearly, help strong engineers move fast without cutting corners, and stay calm and pragmatic under pressure. You care as much about how your team gets to an answer as the answ

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 ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own. 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 AI 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 optimize production AI workloads

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: Modal builds AI infrastructure products that developers love. That's how we grew so quickly and why word-of-mouth remains one of our most important channels today. From powering one of the largest vibe-coding platforms at Lovable to enabling teams like Ramp to build their own internal coding agents , Modal Sandboxes are used by developers to safely execute AI-generated code at scale. We're now hiring our first developer relations engineer focused on Modal Sandboxes. Whether it’s banger tweets , in-depth technical resources or long-form talks , we want to meet developers by any medium necessary and empower them to build and ship novel AI products. In this role, you will primarily be creating and distributing technical content that is unique, educational, and practical. This content will be the first Modal touchpoint for many of our users. We want to not only showcas

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: At Modal, we sell cloud services atop which our customers run their critical production systems. As a rapidly growing new cloud infrastructure company, we seek to improve our reliability dramatically while scaling the size of our platform, customer base, and our team. This role is for people who are deep systems thinkers, love stacking nines, and thrive from making others move faster at scale. Responsibilities include: Identifying architectural changes to improve reliability and performance. Fostering a culture of reliability across Modal’s engineering organization. Defining and implementing operational processes such as deployments, upgrades, etc. Operating systems like Kubernetes, Postgres, Redis, etc. Participating in on-call rotations, and responding to production incidents. Requirements: 5+ years of experience writing high-quality production code. 2+ years of

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

Lithic is the modern card issuing and processing platform empowering ambitious financial companies to build the future of payments. Our infrastructure powers card programs for 100+ innovative clients, from fintechs reimagining credit and digital banking to platforms transforming disbursements and spend management. Companies like Mercury, Flex, and Novo rely on Lithic's developer-friendly APIs, direct network connections, and flawless reconciliation to launch and scale card programs in weeks, not years. We're building a future where access to better financial products materially improves people's lives, free from the constraints of 30-year-old mainframes and legacy processors. We're proud to be backed by world-class investors who share that vision, including Bessemer Venture Partners, Index Ventures, Spark Capital, Stripes, and Mastercard, along with many others. We're a team of 170+ across 26 states and 7 countries, headquartered in New York City. Lithic is hiring a Solutions Engineer to design and deliver technical product solutions that meet customer needs and highlight the value of our platform. In this role, you'll be the technical and strategic bridge between Lithic and our clients. You'll own complex client relationships from pre-sale through implementation, helping partners design integrations, navigate the Lithic platform, and unlock the full potential of card issuing infrastructure. This is a high-impact, highly visible role that requires equal parts technical credibility, client empathy, and cross-functional influence. You’ll be responsible to collaborate with internal teams to develop tailored solutions that clearly demonstrate the benefits of working with Lithic. If you’re passionate about technology, problem-solving, and creating exceptional customer experiences this role is for you. What You'll Do Client Engagement Serve as a trusted technical advisor to a portfolio of strategic clients, from early-stage fintechs to enterprise partne

About the Team OpenAI’s API Platform organization builds the products and infrastructure that help first-party and third-party developers build with OpenAI models. We ship the API primitives, tools, SDKs, documentation, playgrounds, and platform experiences that make OpenAI’s capabilities reliable, understandable, and useful in production. The API Experience team is focused on the end-to-end developer experience for the OpenAI API. We own the surfaces developers touch every day: docs, SDKs, the Playground, examples, onboarding flows, and the systems that help developers go from first request to production deployment quickly and confidently. About the Role We’re looking for full stack and frontend engineers to help define and build the next generation of OpenAI’s developer experience. In this role, you’ll work across frontend product surfaces, backend systems, SDK and documentation pipelines, and API workflows that serve millions of developers and companies. You’ll partner closely with product, design, research, API engineering, and developer-facing teams to make complex AI capabilities simple to understand, easy to test, and safe to launch in real-world applications. This is a highly cross-functional role for someone who cares deeply about craft, developer empathy, reliability, and product velocity. In this role, you will: Build and scale developer-facing products including the OpenAI API Playground, documentation experiences, onboarding flows, examples, and API workflow tools. Own full stack projects end to end, from product definition and UX collaboration through backend implementation, launch, measurement, and iteration. Improve the systems that generate, maintain, and publish SDKs, API references, docs, guides, and developer examples. Partner with API, research, design, and infrastructure teams to bring new model capabilities and API primitives to developers in a clear, usable way. Use developer feedback, product analytics, and direct customer insight to identif

Datadog's Software Engineers with Systems depth leverage their experience with systems and tooling to build software that ensures Datadog remains reliable, performant, and secure. For this track, their Software Engineering experience may resemble the Distributed Systems track, but is typically applied in combination with their systems experience to build and run internal platforms and tools that our products are built on. These people typically have deep experience building and managing large cloud infrastructure deployments, or leading reliability efforts for orgs similar to ours, or building release machinery to allow hundreds or thousands of devs to do their jobs without stepping on each others' toes. The systems and tooling where they may have experience depth may include (but not limited to): bazel, build tooling, cassandra, CDN, chef, configuration management, container orchestration, consul, docker, elasticsearch envoy, haproxy, kafka, kubernetes, load balancing, network architecture, postgres, redis, release management, RPC frameworks, service discovery, spinnaker, terraform, zookeeper. Bonus: You’re excited about leveraging AI tools to enhance how you code, solve problems, and build – or eager to learn how This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government. #LI-KM5 Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan. Th

Yext (NYSE: YEXT) is the enterprise agentic marketing platform. Built on the world's most comprehensive structured data platform for local businesses, Yext gives brands and their partners the visibility intelligence to win every moment of discovery – across AI and traditional search. Yext's API-first architecture connects structured data to APIs, MCP servers, and generative interfaces, so partners and developers can build purpose-built experiences on the same infrastructure powering Yext's own products. Thousands of brands and digital marketing partners in financial services, healthcare, retail, hospitality, and food rely on Yext to manage, measure, and optimize visibility at scale. For more information, visit yext.com . At Yext, Product Engineering builds and evolves the technology behind our products and services. We’re looking for software engineers who want to solve meaningful technical problems, contribute to systems at scale, and help shape what we build next. We work in an agile environment with two-week sprints and regular demos that keep teams aligned and give engineers clear visibility into the impact of their work. From day one, you’ll contribute directly to the codebase and collaborate with experienced engineers from a wide range of leading universities and technology companies. We are looking for an engineer to join Team Watson , which owns and develops the systems that power Yext Search and Yext Chat. The team builds the indexing, retrieval, and serving technology that enables brands to deliver fast, relevant answers across their websites and digital experiences. Yext Search handles more than 50 million requests each month, serving users around the world in over a dozen languages. Watson also brings these search and retrieval capabilities to Yext Chat, helping conversational experiences generate useful answers grounded in trusted customer content. Because Watson’s systems serve real-time, customer-facing experiences at a global scale, engineers o

We are investing in agentic AI and need a Senior AI Engineer to lead the design and delivery of these systems. This is a foundational hire: you will own both the agent-facing workstreams — pipelines, orchestration, conversational interfaces — and the underlying context layer that makes them reliable, including memory management, knowledge graph integration, and retrieval infrastructure. You will work closely with data engineers, project leads, and client stakeholders, and play a key role in shaping how Lynx builds and ships AI solutions at scale. What This Involves: Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows. Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines. Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks. Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders. Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on. Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production. Requirements: 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production. Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.). Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures. Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). Proficiency in

Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Lead the technical design and architecture of major platform initiatives, author design documents and build consensus across engineering teams. Define technical roadmaps for complex, multi-quarter projects that span multiple teams. Make critical architectural decisions for company documentation infrastructure, balancing scalability, reliability, and developer experience. Evaluate and set direction for integrating emerging technologies, including AI/LLM capabilities, into company documentation platforms and authoring tools. Establish and evolve engineering standards, best practices and technical guidelines for the team and broader organization. Partner with engineering teams across the company to understand documentation needs and design integrated solutions. Design, build and maintain scalable, reliable and performant services and systems. Contribute high-quality code across the full stack and navigate codebases with different languages and tools. Debug and resolve complex production issues and improve system reliability. Take ownership of system health and incident response. Who you are Minimum requirements Must have a Bachelor's degree or foreign equivalent in Computer Science, Software Engineering, Engineering, or a related field, plus four (4) years of experience in Software Engineering. Must have four (4) years of experience in each of the following: - Working in a full stack environment with a foc

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Baseten's GTM org is in hyper growth. As it grows and matures, the needs of the GTM stack get more sophisticated with scale — and this team exists to stay ahead of those needs. Our GTM Engineering team plays a critical role in building and maintaining the connective tissue across Baseten’s GTM tools, processes, and user experience for the field. The GTM tooling landscape is changing fast, and the teams that win are the ones that adapt and iterate the fastest. This role exists to make sure Baseten is one of them. You'll design, build, and ship AI-powered workflows that scale our GTM functions as a competitive advantage. We want someone who can walk in, audit what we have, identify what we're missing, and start shipping fast. You know when to reach for Clay and when to build something custom in Claude Code. You think two to three steps ahead about how the thing you build today fits into the broader systems architecture tomorrow. And you bring a point of view — on our stack, on what we should be building, and on where AI can do something low-code tooling simply can't. RESPONSIBILITIES Ship AI-powered workflows for the field — build the agents and automations that give reps and managers real leverage, off-loading the manual and repetitive work. Reach for AI where it does something low-code can't. Get insights in front of reps — turn Salesforce, warehouse, and usage data into the dashboards, scores, and alerts reps