Software Engineer Product Security Data Platforms in United States

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. We are looking for a highly motivated senior software engineer for an exciting role in our communication libraries and network software team. The position will be part of a fast-paced crew that develops and maintains software for complex heterogeneous computing systems that power disruptive products in High Performance Computing and Deep Learning. What you will be doing: Design, implement and maintain highly-optimized communication runtimes for Deep Learning frameworks (e.g. NCCL for TensorFlow/Pytorch) and HPC programming interfaces (e.g. UCX for MPI/OpenSHMEM) on GPU clusters. Participating in and contributing to parallel programming interface specifications like MPI/OpenSHMEM. Design, implement and maintain system software that enables interactions among GPUs and interactions between GPUs and other system components. Creating proof-of-concepts to evaluate and motivate extensions in programming models, new designs in runtimes and new features in hardware. What we need to see: M.S./Ph.D. degree in CS/CE or equivalent experience. 5+ years of relevant experience. Excellent C/C++ programming and debugging skills. Strong experience with Linux. Expert understanding of computer syst

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role Over the past few years at Ramp, we've reimagined how businesses manage spend, and the backbone of that vision is Procure-to-Pay. The P2P team builds the systems that connect vendors, invoices, and payments into one seamless, automated flow. It's where billions of dollars move through our platform, and every line of code matters. We're looking for a backend engineer who loves complex systems, obsesses over correctness, and wants to shape how money moves in modern finance software. You'll help design and scale the infrastructure that powers our payment rails, ensures auditability across products, and unlocks the next wave of AI-driven automation. It's a chance to work on systems that actually move money, with a team that cares deeply about precision, reliability, and scale. The problems are tough, the stakes are high, and the impact is massive. What You'll Do Design and build core P2P systems, from invoice ingestion and approval workflows to payment orchestration and reconciliation logic. Enable AI agents to classify, validate, and pro

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. Team Description: Plaid is evolving into an AI-first company, and Intelligent Tooling sits at the center of that transformation. The Intelligent Tooling team is being built from the ground up, and our mission is to establish the technical foundations, operating model, and internal platforms that embed AI deeply into Plaid’s coding tools, internal systems, and the entire software development lifecycle. When we are successful, engineers across Plaid will delegate lower-leverage work to AI agents, move faster with confidence, and spend more of their time designing and inventing for customers. Intelligent Tooling owns the platforms and systems that make this possible - from AI coding integrations and SDLC agents to the internal tools that power Plaid’s operations. Role Description: As a Staff Software Engineer on the Intelligent Tooling team, you will build and operate internal systems that directly impact how engineers across Plaid do their work, and own the technical direction for major parts of that surface. This is a hands-on role with significant ownership, where success is measured by real adoption, reliability, and improvements to developer experience. You will work on AI-powered tooling, interna

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. AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices. We believe, in the foreseeable future, AI will revolutionize the FinTech industry - from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape. Plaid in the FinTech AI Ecosystem Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem. We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI-powered products should feel seamless and embedded for the end users. The list goes on. The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI-powered products and solutions t

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. The Data Governance team makes sure Plaid handles consumer and customer data responsibly — and can prove it. Our mission is to enforce Plaid's privacy commitments and regulatory obligations in the systems themselves rather than in policy documents: we build the platform and controls that govern how data flows through Plaid — where it lives, who can use it, for what purpose, and for how long. That includes verifiable deletion of consumer data on request, enforcement of data-use restrictions so downstream systems can only use data in permitted ways, and the cataloging and classification that let Plaid know what data it holds and how sensitive it is. We operate at the scale of Plaid's entire data footprint, and correctness and auditability matter to us as much as throughput. As a Staff Software Engineer on Data Governance, you will set the technical direction for how Plaid enforces data governance at scale. You'll lead the design of distributed backend systems that reliably delete, restrict, and track data across dozens of services, making architectural decisions whose blast radius spans the whole company. You'll drive multi-quarter initiatives from ambiguous privacy and regulatory requirements through

We are a global team of innovators and pioneers dedicated to shaping the future of observability. At New Relic, we build an intelligent platform that empowers companies to thrive in an AI-first world by giving them unparalleled insight into their complex systems. As we continue to expand our global footprint, we're looking for passionate people to join our mission. If you're ready to help the world's best companies optimize their digital applications, we invite you to explore a career with us! Your opportunity The Data, Identity, & API Platform group at New Relic builds the foundation for all of our products: data ingest, storage, and query. As an engineer working on NRDB, you’ll be contributing directly to the proprietary telemetry database technology at the core of our business. We own our software from top to bottom and are directly responsible for its quality and reliability. Each member of the team shares our pager rotation and will occasionally be on-call to respond to system failures; so we prioritize work that keeps the lights on and the pager quiet, in addition to the work that powers all of our new products and streams of data. If the idea of working on systems that process millions of messages per second and handle petabytes of data excites you, then you may be an excellent fit! What you'll do Own the New Relic query language and gateway stack Proactively participate in cross-functional committees to move the query language and gateway forward, ranging from collaborations with AI, Visualizations, and Data Processi

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Software Engineer on the Communications team, you will work on real-time communication across 2D and 3D spaces for billions of users while keeping interactions civil, safe, and expressive. Communication is at the core of what makes Roblox a vibrant social platform, the systems you build will define how tens of millions of people connect every day. You will work with teams across Game Engine, Safety, and ML Platform, building backend services at scale, data pipelines, and ML models that strengthen the underlying chat and policy infrastructure. You will build and ship detection systems that identify chat violations and continuously evolve to outpace bad actors circumventing our safety policies. As a core contributor to a broader suite of communication products, you will drive feature development while embedding safety into the foundation of the platform. If you are a developer with an understanding of large scale systems and want to shape how the next generation of Roblox users interact safely in the metaverse, you’ll be right at home within our highly-skilled and rapidly growing team. You will: Engineer and scale the communication infrastructure that supports millions of c

About the Team The Workload Networking team is responsible for the collective communication stack used in our largest training jobs. Using a combination of C++ and CUDA we work on novel collective communication techniques that enable efficient training of our flagship models on our largest custom built supercomputers. The models we train are key ingredients to the AI research progress at OpenAI and the field as a whole, and we continually incorporate learnings from our entire research org into our training platform. About the Role As a Software Engineer, Networking you will design and implement custom networking collectives that are tightly integrated into our training stack. We’re looking for people who have a background in low level performance critical software. Experience with collective communication is a bonus. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Collaborate closely with ML researchers to design and implement efficient collective operations in C++ and CUDA. Ensure that our largest training jobs take full advantage of the different network transports used in our supercomputers. Work on simulations to inform our future supercomputer network designs. You might thrive in this role if you: Have written distributed algorithms using RDMA in the past. Are comfortable writing low level performance sensitive CPU and/or GPU code. Are familiar with network simulation techniques. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voic

About the Team Business Systems / Enterprise Platform Technology builds the internal systems, data foundations, workflow infrastructure, and enterprise platforms that help OpenAI operate at scale. The EPT AI Pod builds AI-native internal apps, MCP connectors, multi-agent workflows, and reusable platform capabilities across Finance, People, and GTM. About the Role As an Enterprise Applied AI Engineer, you will build internal apps for enterprise operations and the shared platform components those apps run on. This includes MCP connectors, multi-agent orchestration, data architecture, evals, monitoring, auditability, and governance. We’re looking for a hands-on engineer who is strong in Python, system design, enterprise integrations, data architecture, and applied AI systems. You should be excited to turn ambiguous business workflows into reliable internal products and shared infrastructure. In this role, you will: • Build internal apps for enterprise operations across Finance, People, and GTM • Build MCP connectors and enterprise integrations with strong auth, permissions, idempotency, retries, and rate-limit handling • Design end-to-end multi-agent workflows with tool routing, human approvals, audit trails, and safe action boundaries • Design data architecture for operational AI systems, including ingestion, schemas, quality checks, lineage, and governance • Build evals, monitoring, metrics, and regression tests for agentic workflows • Create reusable infrastructure, patterns, and components that other enterprise teams can build on • Partner with system owners and business owners to turn messy enterprise workflows into reliable internal products You might thrive in this role if you: • Have strong Python engineering skills for backend services, MCP connectors, agent/tool workflows, eval harnesses, and data ingestion jobs • Have strong system design skills across shared infrastructure, app architecture, reliability, and scaling • Have experience building internal apps,

About the Team The Support Automation team at OpenAI scales the organization by applying cutting-edge AI models to real-world challenges, automating and enhancing work across the organization. From customer operations to engineering, we develop an ecosystem of automation products that empower our colleagues and drive impact. We're passionate about crafting products that serve those around us, blending rapid prototyping with a focus on long-term quality and reliability. By creating reusable solutions, we create patterns that can be applied across diverse domains within OpenAI. TLDR: this team leverages OpenAI technology to improve OpenAI, and you’ll have the opportunity to leverage the full extent of our tech (both public and pre-released) to accomplish this mission. About the Role We’re looking for a Backend Software Engineer with experience working in ML/LLM-heavy domains to help to design and build an evals infrastructure that measures the quality of OpenAI’s support automation. This is a deeply technical and highly cross-functional role where you’ll build robust systems and backend services that serve as the foundation for how knowledge is created, accessed, and applied across OpenAI. The role will especially focus on working closely with Data Science and Research partners to design and build evals at scale. In this role, you will: Design eval pipelines that are reliable, reproducible, and extendable Build the infrastructure for continuous eval monitoring frameworks (regression/drift monitoring, building robust golden datasets) along with feedback loops that ultimately strengthen support automation Design, build, and maintain backend services and APIs to support intelligent automation and knowledge systems Integrate and structure data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows. Collaborate closely with data, research, and engineering teams to integrate OpenAI models into high-leverage workflows

About the Team The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization. About the Role We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes). Key Responsibilities Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar. Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts. Deep-dive performance on distributed training/inference: Collective performance and tuning (across NCCL/RCCL and internal libraries) Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection). Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops). Work cross-functionally with vendors and internal stakeholders by producing

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve o

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role We’re rolling out Go support at production scale at Notion, and we need an owner who can make it durable. You’ll lead the work to turn Go into a fully supported, well-operated platform: reliable and scalable service patterns, paved paths for our tooling stack, and the guardrails that make building in Go feel fast and safe. This role matters because our next wave of AI and agent-driven products will require backend services where Node won’t always be the right fit, and the platform decisions we make now will compound for years. While Go is the core focus, this role sits within Developer Experience and will regularly tackle other high-leverage engineering productivity challenges, developer experience ranging from AI-assisted development workflows and remote agent environments to CI performance, deployments, and reliability tooling. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Lead Software Engineer Our Purpose We connect and power an inclusive digital economy that benefits everyone, everywhere. We enable safe, simple, smart, and accessible transactions through secure data, strong networks, partnerships, and innovation. Our culture is driven by our Decency Quotient (DQ), fostering inclusion, respect, and diversity. We believe that diverse perspectives strengthen decision-making, drive innovation, and deliver better business outcomes. About the Role The Mastercard Authentication Program owns how consumer authentication works for both in-store and e-commerce transactional use cases. The primary purpose of this role is to develop and deliver best-of-breed authentication products for e-commerce transactional use cases that will drive uptake and penetration for the products and revenue for Mastercard. The authentication products that fall within this role’s responsibilities are ID Check, Token Authentication Service and Token Authentication Framework. If you want a challenging project that is changing the way people make payments, this is the role for you! Role As a Lead Software Engineer, you will be responsible for: • Design and develop secure, reliable, and scalable solutions for globally distributed customer-facing products • Support dev

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Software Engineer Job Overview: Responsible for the analysis, design, development, testing, and delivery of secure, scalable software solutions. Define requirements for new applications and customization adhering to Mastercard standards, processes, and best practices. Develop, customize, and test applications to integrate to Mastercard specifications. Provide leadership, mentoring, and technical training to other team members. Major Accountabilities • Plan, design, architect, and develop secure, scalable, and maintainable technical solutions and alternatives to meet business requirements in adherence with Mastercard standards, processes, and best practices • Lead day-to-day system development and maintenance activities of the team to meet service level agreements (SLAs) and create solutions with a high level of innovation, cost effectiveness, quality, reliability, and faster time to market. • Accountable for the full systems development life cycle including creating high-quality requirements documents, use cases, designs, and other technical artifacts including but not limited to detailed test strategies, performance benchmarking, release rollout and deployment plans, contingency/back-out plans, feasibility studies, cost and time analysis, and detailed estimates. • Design, develop, test, dep