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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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Plaid’s mission is to unlock financial freedom for everyone by making money movement and access to financial data simple and secure. As a Fullstack Software Engineer, you will design and build the systems and experiences that power how millions of people connect to their finances. You will work across the stack, building scalable backend services and APIs while also crafting intuitive, high-quality frontend experiences that bring those systems to life. This role is ideal for engineers who enjoy switching between backend problem-solving and frontend user experience work, and who are excited to grow their impact across both. You will collaborate closely with product managers, designers, and other engineers to ship products that are reliable, secure, and delightful to use. At Plaid, engineers take ownership early, contribute to architectural decisions, and see their work reach millions of users. Responsibilities: Build across the stack. Design, develop, and maintain scalable backend services and APIs, as well as intuitive, high-quality frontend applications that bring those systems to life. Collaborate cross-functionally. Partner closely with product managers and designers to define

As a Software Engineer - Frontend on a feature team, you'll be responsible for building an intuitive, responsive product. Your work will help thousands of customers to monitor the health and performance of their systems, no matter the scale. In this capacity, you’ll use tools like React and Typescript to help streamline the flow of data from platform to user, enable seamless pivots from one view to the next, and build a powerful yet easy-to-use platform. You will work on complex frontend challenges for thousands of customers with huge sets of data and work on exciting scalability and performance challenges. Our users are also developers so you will feel close to the product and have an impact on the development world. You will be part of a front-end community of 200+ passionate frontend engineers and you will be surrounded by experts. Join us to build the next generation of high-scale, data-powered features for our customers. 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: Work closely with backend engineers, product managers and designers to inform/refine/validate design concepts Break down complex features into distributable engineering tasks and rollout plans Design and deliver features that have direct impact on thousands of users Experiment with and advocate for new systems, design patterns, and tooling Participate in hackathons, sync with other Front end engineers at the monthly demos meeting and yearly Front End summit Use and contribute to a best-in-class in-house design system Own meaningful parts of our service, improve performance and address scalability limits Work in a fast-paced, high-growth environment that values diversity of talent, excellence of product, and exciting engineering challen

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. In this role you will: As a Hardware Test Engineer, you will work on Machine Learning/AI hardware system projects to craft the solutions for current and future data center deployments. You will bring a strong understanding of hardware system testing, excellent project management skills, and the ability to collaborate across multiple teams to ensure efficient lab operations. You will be responsible for designing, implementing, and executing comprehensive test plans that ensure the reliability, performance, and scalability of our supercomputing hardware systems. You will develop detailed test plans and methodologies tailored to hardware components, including processors, memory modules, custom accelerators and interconnects. You will collaborate with hardware design, manufacturing, firmware teams and vendors to identify, analyze, and resolve issues affecting hardware, power, thermal and high-speed interconnects. You will perform in-depth debugging on the hardware system Excellent analytical skills to diagnose hardware issues, troubleshoot problems, and propose solutions. Ability to interpret complex test data, identify trends, and draw meaningful conclusions. High-speed links, with a focus on SerDes (Serializer/Deserializer) technology to assess signal integrity, error rates, and overall link performance. You will collaborate with the lab manager to maintain the equipment and hardware systems, including oscilloscopes, thermal test chambers, liquid cooling systems, and other mea

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About The Role The Security Team is responsible for securing all things Sentry: our customers, our code, and everything in between. We are a small but growing team with broad scope, high trust, and the autonomy to tackle hard security problems with creativity and an engineering mindset. We work at a company with a strong developer culture, building a product that millions of developers genuinely love and rely on. That context shapes everything about how we operate. We take a pragmatic approach to preventing and responding to security risks. In this role not only will you build and contribute to systems which detect malicious activity, you will have the unique opportunity to implement new controls to prevent future incidents. You will work across detection and response and corporate security domains. You'll contribute to practices that keep Sentry secure as we grow: alert triage for corporate and production, detection engineering, deploying preventative controls, identity and access management, investigations and incident response, and more. You'll partner with teams across the company to prevent and respond to security incidents. You will work as a technical collaborator who prioritizes preventative controls, defense in depth, and high signal alerting practices. As Sentry expands our agentic product capabilities and development practices, you'll also find yourself at the frontier of a new set of security approaches and challenges. In this role, you will Maintain, improve, and own detection engineering systems. We own and operate our own detection stack and are building agentic triage with thoughtful security response and orc

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About The Role The Security Team is responsible for securing all things Sentry: our customers, our code, and everything in between. We are a small but growing team with broad scope, high trust, and the autonomy to tackle hard security problems with creativity and an engineering mindset. We work at a company with a strong developer culture, building a product that millions of developers genuinely love and rely on. That context shapes everything about how we operate. As a Security Engineer on this team, you'll work across application and platform security domains. You'll contribute to the practices that keep Sentry secure as we grow: security reviews, threat modeling, vulnerability management, and embedding secure coding practices into an engineering organization that cares about doing things right. You'll partner closely with product and engineering teams to influence how features are designed and built from the start. You will work as a technical collaborator who helps make the secure path the obvious one. As Sentry expands our agentic product capabilities and development practices, you'll also find yourself at the frontier of a new set of security challenges. In this role, you will Support and help mature Sentry's security review program. From secure code review, to architecture review, and threat modeling. You'll help build the processes, tooling, and culture which make security a natural part of how we ship and operate. Contribute to mature vulnerability management practices. Intake, triage, prioritization, remediation tracking, and support of our bug bounty and responsible disclosure program. Advocate for secure-by-desig

About the Team OpenAI's People team helps hire, develop, and support the people building safe and beneficial AGI. Within that team, People Systems builds the technical foundation that enables our HR, recruiting, payroll, benefits, and performance operations to scale with quality, speed, and rigor. We work at the intersection of HR systems, software engineering, and internal tooling. Our goal is not just to keep core systems running, but to build durable technical leverage for the company. About the Role We're hiring a Workday Engineer to help design, build, and operate the systems that power critical people workflows at OpenAI. This is a highly technical role for someone who combines strong Workday expertise with real engineering fluency. You'll build reliable integrations, improve system architecture, automate complex workflows, and help connect Workday to internal tools, external platforms, and emerging AI-driven systems. You should be comfortable going beyond configuration work. We're looking for someone who can reason through ambiguous systems problems, write and debug technical solutions, work effectively in Git-based environments, and use modern developer workflows, including CLI-driven tooling, to build and operate with speed and discipline. You'll partner closely with cross-functional teams across People, Finance, Security, and Engineering, including our People Innovations team, to build systems that are secure, scalable, and practical. Some work will involve improving mature production infrastructure; some will involve building entirely new workflows and capabilities from scratch. In this role, you will Design, build, and maintain Workday integrations, applications, and workflow automations across domains such as payroll, benefits, recruiting, performance, and case management Improve the reliability, quality, and scalability of People systems through strong engineering, testing, and operational practices Build technical solutions that connect Workday with i

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role You will build the model runtime within the inference engine that executes complex, frontier models at scale on OpenAI’s custom silicon. The runtime will sit between models running on the hardware and the upper layers of the cluster serving software stack, translating demanding inference workloads into efficient execution while optimizing for throughput, latency, utilization, and reliability. You will work across model architecture, distributed systems, compilers, kernels, and silicon to design a production-grade runtime comparable in ambition to systems such as vLLM and SGLang, but customized and optimized for OpenAI’s AI accelerator. Your work will shape how new model capabilities map onto the platform and how quickly custom silicon can deliver meaningful performance in production. In this role, you will: Design and implement the LLM inference runtime for frontier models running on custom silicon. Build scheduling, continuous batching, memory management, KV-cache management, and execution orchestration for high-performance inference. Develop distributed execution strategies across chips, hosts, and racks, including model partitioning, communication, and synchronization. Optimize end-to-end latency, throughput, memory efficiency, and hardware utilization across diverse model architectures and serving workloads. Partner with kernel, compiler, architecture, and silicon teams to co-design interfaces and remove performance bottlenecks across the stack. Enable new

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role You will build the low-level device runtime that turns compiled programs into efficient, functional and performant execution on OpenAI’s custom AI accelerator. This software will schedule kernel launches, manage device memory and address spaces, coordinate synchronization, and expose reliable abstractions to higher-level runtimes and frameworks. You will work at the boundary of software and hardware, partnering with compiler, kernel, architecture, verification, and silicon teams to define interfaces and validate behavior. You will also use and improve event-based, cycle-accurate simulation to develop runtime capabilities before silicon is available, diagnose performance and correctness issues, and guide hardware-software co-design. In this role, you will: Design and implement the low-level device runtime for OpenAI custom silicon. Build kernel-launch scheduling, command submission, queueing, dependency tracking, and completion handling. Manage device memory spaces, allocation, virtual-to-physical mappings, data movement, and lifetime across concurrent workloads. Implement synchronization primitives, events, barriers, streams, and ordering guarantees that are correct and efficient. Define clean interfaces between the runtime, drivers, firmware, compiler-generated code, kernels, and higher-level execution systems. Use event-based, cycle-accurate simulators to develop, validate, debug, and performance-tune runtime behavior before and after silicon availability. Di

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: As a Developer Platform engineer, you will directly contribute to the foundational pieces that make Notion extensible and connected. You will build tools, APIs, and platform experiences that help customers connect Notion to the world — bringing their apps, data, and workflows into one workspace. Your work will make it easier for developers, admins, and builders to create reliable integrations and automations on top of Notion. You will be a key player in building the robust technical foundation that allows Notion to achieve the connected workspace vision. Your work will include both internal platform contributions that accelerate other Notion engineering teams and end-user-facing functionality that enables toolmaking ubiquity. You will be presented with challenging technical problems, as Notion’s product needs are complex. You’ll play a key role in identifying and executing against technical investments that ensure the long-term quality, reliability, and performance of Notion’s platform as we scale. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursd

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. Build Core Data Engineering Primitives at Cloud Scale Data pipelines are foundational infrastructure — when they're fast, correct, and maintainable, customers build on them with confidence. If you've spent the bulk of your career building large-scale data infrastructure — designing streaming or transformation primitives, reasoning hard about consistency and fault tolerance, and owning the systems that run under millions of customer workloads — this role might be for you. You'll be working on the streaming and transformation layer at Snowflake: the constructs that define how customers move, shape, and maintain data. AI has a real presence in this work — in how customers use these pipelines and in how we think about building them — but the core job is hard distributed systems engineering, and that's what we're hiring for. About the Team We build the core data engineering primitives that power Snowflake's streaming and transformation capabilities. From the constructs customers use to define real-time pipelines to the execution fabric that makes those pipelines reliable and cost-efficient at cloud scale, our team owns the full stack of declarative data engineering. We're a small, high-ownership team operating close to the product — which means your decisions ship, your architec

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. Plaid Protect is a real-time fraud intelligence product built on a unique advantage: Plaid’s network-level visibility across bank accounts, devices, identities, sessions, institutions, applications, and financial behavior. Protect helps customers detect first-party fraud, synthetic identities, account takeovers, and coordinated attacks that are difficult to see from a single application, account, or transaction. Trust Index turns that fraud intelligence into real-time fraud scores and actionable attributes. This team builds the systems that make this intelligence possible: low-latency inference, new data and model integrations, customer-facing APIs and attributes, safe rollouts, and feedback loops. Ti3 expanded Plaid’s fraud graph nearly 10x and, in early testing, detected up to 41% more fraud at the same false-positive rate. Learn more about Ti2 and Ti3 . We are a small, high-agency team working closely with Product, Data Science, and Machine Learning. We value demos over docs, conviction over consensus/alignment, builder schedule over meeting-heavy calendars. We’re scrappy and a talent-dense team that has high agency and high ownership. As a Staff Software Engineer on the Protect Core team, you wi

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: We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy. What You'll Do: Platform & Runtime Security Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments) Strengthen boundaries between customers, workloads, and internal systems Identify and mitigate risks in distributed, dynamic compute environments Container &