Software Engineer Distributed Systems Jobs

About Vercel: Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. About the Role: The Scheduled Tasks team builds the platform primitives that let applications and agents run work now, later, or for a long time. We own Vercel Workflows, Queues, and Cron: the systems developers use to build long-running, event-driven, and scheduled applications. You will join a team working at the intersection of developer experience and distributed systems. Together, we are building and scaling the products that make it straightforward for developers to coordinate background work, move messages between services, and schedule work with confidence. You will collaborate closely with engineers across Vercel to make these powerful capabilities feel simple, composable, and native to the platform. What You Will Do: Design, build, and operate platform capabilities across Workflows, Queues, and Cron. Help developers build applications and agents that coordinate background, event-driven, and scheduled work. Build APIs, SDKs, and tooling that make it easy to define, run, and manage durable work at scale. Work on the distributed-systems foundations behind scheduling, message delivery, execution, retries, and state. Partner with product, developer experience, and infrastructure teams to turn customer needs into clear, useful developer primitives. Raise the engineering bar through thoughtful design reviews, well-tested code, production ownership, and clear technical communication. Engage with developers and the open-source community to understand where background jobs, queues, and scheduling create friction—and use that feedback to improve th

Enterprise Advanced is a distributed team across Europe and India that builds the software running MongoDB on any infrastructure, at global scale — from on-prem data centers to private cloud. You'll work primarily on Ops Manager and Automation, the systems that let customers deploy fault-tolerant, globally distributed MongoDB clusters in minutes. Our software manages some of the largest self-managed MongoDB deployments in the world, with production clusters running hundreds of shards and nodes under a single deployment. The main focus of this team is to adapt our software to manage MongoDB clusters which are deployed in data centers or private cloud platforms. You will work on the core functionality for all of our products, mainly on the Ops Manager , and Automation products. Our team's end users are some of the largest businesses in the world, deploying massive clusters and processing huge amounts of data. This role is based in our Gurgaon office, and can work in a hybrid fashion. This role will report to the Senior Engineering Manager also based in our Gurgaon office. What you’ll do Design, implement, test, and release features for Ops Manager Own end-to-end delivery of complex projects, from design through incremental shipping Troubleshoot and resolve issues surfaced in customer deployments running at scale Apply engineering judgment and MongoDB's core values across planning, design, and code review A great fit for this role will be You enjoy distributed-systems problems; consistency, fault tolerance, and scale are the daily reality, not edge cases People who like ambiguity and are comfortable defining their own approach with guidance, not step-by-step instruction You're flexible! You're willing to take on a wide variety of responsibilities, learning as you go You're a self-starter! You're comfortable organizing your own time, acting on feedback and prioritizing with guidance from senior members of your team Requirements 4+ years experience with a language

About the Team The GPT Infrastructure team builds systems that turn advances in model inference and optimization into reliable production capabilities. We enable OpenAI workloads to be qualified and optimized across new accelerator platforms without requiring a one-off port and tuning effort for every hardware target. Our work spans distributed systems, model execution, compilers and runtimes, performance engineering, secure partner integrations, evaluation systems, and developer tooling. We build the infrastructure that makes optimization workflows automated, reproducible, and trustworthy. About the Role We are seeking a software engineer to help build the platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop both OpenAI-hosted services and secure partner-side software for running long-lived optimization workflows. These workflows generate candidate kernels, runtime configurations, and serving-stack changes; compile and execute them on target hardware; verify their correctness; measure their performance; and use the results to guide further optimization. You will work across model architecture, distributed execution, compilers, runtimes, networking, and accelerator systems. A central part of the role is turning research prototypes and one-off hardware bring-up efforts into reliable, reusable infrastructure with clear contracts, reproducible results, strong observability, and well-defined security boundaries. Key Responsibilities Design, build, and operate APIs and control-plane services for long-running workload qualification and optimization campaigns, including scheduling, retries, checkpointing, resource budgets, and observability. Build secure partner-side execution and evaluation software that can compile, run, verify, profile, and benchmark candidate artifacts on accelerator hardware. Integrate model workloads, hardware profiles, compiler toolchains, runtimes, serving engines, and distributed-exe

About the Role: Tubi's content platform is the engine behind one of the largest free streaming services in the world. Every play, every deal, every creator, every frame of video flows through systems CPE owns, and the surface area is enormous. Distributed services running on the hottest path of Tubi's traffic. Video pipelines processing one of the largest workloads in streaming. Workflow engines automating the operations that used to consume entire teams. Creator-facing products turning a back-office process into a real platform. And on top of all of it, an AI-native rebuild of the CMS that most companies aren't willing to attempt. This isn't a single-domain role. It's a platform where backend, frontend, video, infrastructure, and applied AI all collide at the scale where decisions actually matter, where an architectural choice ripples across millions of titles and billions of requests, and where the difference between "good enough" and "great" shows up in revenue. We're looking for builders who want to range across domains — backend one quarter, frontend the next, applied AI the one after that — and who want their work to be felt: by viewers when a title plays instantly, by creators when they go live the same day, by Content Ops when a workflow runs itself, and by the business when the platform stops being a cost center and starts being a force multiplier. The infrastructure is already there. The mandate is already there. What's missing is the people who want to build the thing, not talk about it. Come build it. This is a hybrid role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: You'll work on systems that sit at the heart of Tubi's business, where the content pipeline meets the viewer, the creator, and increasingly, the AI agent. The work spans the full stack of a modern content platform: distributed services, video infrastructure, workflow automation, and applied AI, all running at

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 Inference Stack team builds the distributed runtime that powers large-scale LLM inference across our platform. We operate at the intersection of distributed systems, model performance, infrastructure, and developer experience. We enable customers to deploy and operate cutting-edge LLM models with industry-leading performance, scalability, reliability, and ease of use. As a Software Engineer on the Inference Stack team, you’ll work across the stack - from the developer experience customers use to deploy models, the libraries used for features like tool calling and reasoning, all the way down to the systems we use to orchestrate deployments in Kubernetes and route traffic efficiently. This is an ideal role for engineers who enjoy owning systems in production, solving hard integration problems, and making complex infrastructure simple and reliable for users. EXAMPLE INITIATIVES Blog Posts https://www.baseten.co/blog/nvidia-dynamo-day-baseten-inference-stack/ https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/ https://www.baseten.co/blog/how-baseten-multi-cloud-capacity-management-mcm-powers-cloud-self-hosted-and-hybr/#comparing-deployment-options-cloud-vs-self-hosted-vs-hybrid RESPONSIBILITIES Develop infrastructure and orchestration systems for deploying and managing large-scale distributed LLM inference Work across the stack, from customer-facing features to low-le

About the Team Our London-based team builds the backend systems that help ChatGPT scale reliably. We work on infrastructure close to the product, partnering with engineering teams to improve the performance, resilience, and operability of critical user-facing systems. Our work combines backend software engineering with distributed systems and production reliability. We build shared capabilities, improve high-traffic workflows, and make it easier to introduce new product functionality without compromising performance or availability. About the Role This role is for software engineers who want to build and evolve backend systems operating at significant scale. You’ll write production code, design shared infrastructure, and solve technical challenges involving performance, distributed systems, and system reliability. You’ll also own how those systems behave in production: how changes are rolled out, how issues are detected and diagnosed, and how recurring operational problems can be addressed through better software and system design. This is a strong fit for backend engineers who enjoy complex systems problems and want a direct connection between the infrastructure they build and the experience of ChatGPT users. In this role, you will: Design, build, and maintain backend systems supporting high-traffic ChatGPT experiences. Develop shared services, APIs, and infrastructure that help product teams build and launch new capabilities safely. Improve the performance, scalability, and efficiency of production systems as usage and product complexity grow. Build and improve systems for asynchronous processing and other large-scale backend workloads. Lead architectural improvements and infrastructure migrations while maintaining correctness, compatibility, and safe rollout and rollback. Strengthen monitoring, alerting, and diagnostics to detect problems early and reduce customer impact. Participate in on-call, incident response, and root-cause analysis, and turn operational lea

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About the Role Anyscale is seeking a Staff Software Engineer to lead the technical vision for our Infrastructure team. As a Staff Engineer, you will be responsible for the architectural evolution of our control plane and data plane, ensuring that our "infinite laptop" vision scales to meet the most demanding distributed AI workloads in the world. You will act as a force multiplier, setting the standards for Kubernetes-based cloud-native infrastructure while mentoring engineers and driving cross-functional alignment across the Ray open-source community and our proprietary product teams. Key Responsibilities Architectural Leadership: Define and drive the multi-year technical roadmap for services that orchestrate Ray clusters across diverse cloud and on-premises environments. Systemic Optimization: Lead the design and optimization of high-performance control plane components specifically tailored for large-scale, heterogeneous AI/ML workloads. Platform Reliability: Establish the organization-wide standards for the reliability, scalability, and observability of Anyscale-managed infrastructure. Strategic Integration: Direct the long-term strategy for accelerator integration (GPUs, TPUs) and container management to ens

MongoDB’s Storage Layer Services (SLS) team is re-architecting the MongoDB cloud storage layer and sits at the heart of our next-generation cloud storage architecture. This relatively new team is building performant, multi-tenant distributed storage services that both enhance today’s Atlas storage stack and enable more customer workloads to run more efficiently. We are looking for a talented Senior Software Engineer to join our team as we execute on a multi-year roadmap and prepare to launch and rapidly scale services that handle petabytes of data. Come do some of the most interesting work of your career as we Think Big and Go Far for our customers! This role can be based out of our New York City office (hybrid working model) or remotely in the North America region. What you’ll do Design, build, and operate control plane services powering an elastic and multi-tenant storage layer for thousands of database instances. Solve problems around maintaining high availability and performance during load spikes, hardware failures, cloud provider outages, and other disruptions. Contribute to a culture of operational excellence through dashboards, playbooks, and on-call improvements. Lead complex technical projects from planning through successful deployment with clear stakeholder updates. Partner closely with peers across database, cloud, and infrastructure engineering teams as well as project management to investigate incidents and develop long-term roadmaps. Mentor junior engineers and foster a collaborative team environment. We’re looking for someone with 5+ years of professional software development experience building, deploying, and operating multi-tenant cloud services with a focus on operational excellence. Experience with large backend/compiled codebases, such as Rust or C/C++. Experience with containerization and orchestration platforms (e.g. Kubernetes). Experience with observability tooling (e.g. time series metrics, dashboards). Experience with distributed systems

At Bolna, we’re building tools that change how businesses leverage voice AI. We’re looking for a Software Engineer to build reliable, scalable systems that power millions of production conversations across languages, industries, and telephony environments. This is a high-impact, high-ownership role where you’ll work on core platform problems across distributed systems, real-time communication, developer infrastructure, and customer-facing products. Our team includes IIT alumni with experience at Bain, Atlassian, Uber, Zomato, and LinkedIn, and is backed by leading investors. Responsibilities Build systems that operate at scale: Design and build backend services that support high-volume, real-time voice AI conversations with strong reliability, performance, and fault tolerance. Own features end to end: Take problems from product requirements and technical design through implementation, testing, deployment, monitoring, and iteration. Improve platform reliability: Build systems that are observable, resilient, and easy to debug. Identify bottlenecks, reduce failure rates, and improve system availability. Work on real-time infrastructure: Solve problems across telephony, streaming audio, webhooks, queues, scheduling, concurrency, and low-latency communication. Build for developers and customers: Improve APIs, SDKs, integrations, dashboards, and internal tools that make the Bolna platform easier to use and operate. Raise the engineering bar: Contribute to technical design reviews, code quality, testing standards, documentation, incident response, and engineering best practices. Required Skills Strong engineering fundamentals: Solid understanding of data structures, algorithms, databases, networking, operating systems, and distributed systems. Backend development experience: 2+ years of experience building and operating production backend systems using Python, Go, Java, Node.js, or a similar language. Production ownership: Experience shipping software to production and own

About the Team At OpenAI, we’re building safe and beneficial artificial general intelligence. We deploy our models through ChatGPT, our APIs, and other cutting-edge products. Behind the scenes, making these systems fast, reliable, and cost-efficient requires world-class infrastructure. The Caching Infrastructure team is responsible for building a caching layer that powers many critical use cases at OpenAI. We aim to provide a high-availability, multi-tenant cache platform that scales automatically with workload, minimizes tail latency, and supports a diverse range of use cases. We’re looking for an experienced engineer to help design and scale this critical infrastructure. The ideal candidate has deep experience in distributed caching systems (e.g., Redis, Memcached), networking fundamentals, and Kubernetes-based service orchestration. In This Role, You Will: Design, build, and operate OpenAI’s multi-tenant caching platform used across inference, identity, quota, and product experiences. Define the long-term vision and roadmap for caching as a core infra capability, balancing performance, durability, and cost. Collaborate with other infra teams (e.g., networking, observability, databases) and product teams to ensure our caching platform meets their needs. You Might Thrive In This Role If You: Have 5+ years of experience building and scaling distributed systems, with a strong focus on caching, load balancing, or storage systems. Have deep expertise with Redis, Memcached, or similar solutions, including clustering, durability configurations, client-side connection patterns, and performance tuning. Have production experience with Kubernetes, service meshes (e.g., Envoy), and autoscaling systems. Think rigorously about latency, reliability, throughput, and cost in designing platform capabilities. Thrive in a fast-paced environment and enjoy balancing pragmatic engineering with long-term technical excellence. About OpenAI OpenAI is an AI research and deployment company d

About the Role The Engineering Acceleration Delivery / Continuous Deployment team builds and operates the systems that safely ship OpenAI’s infrastructure and product code to production. We own the deployment platform, release pipelines, and rollout safety mechanisms that allow engineers across OpenAI to deploy changes rapidly while minimizing operational risk. Our mission is to make production deployments fast, safe, and increasingly autonomous. This role sits at the intersection of developer productivity, distributed systems reliability, and large-scale infrastructure orchestration. In This Role, You Will Design and build continuous deployment infrastructure that safely rolls out changes across dozens of Kubernetes clusters and global regions. Develop systems for progressive delivery, including canary releases, staged rollouts, and automated rollback. Improve engineering velocity by reducing friction in the release pipeline and automating manual operational workflows. Work with product and infrastructure teams to ensure their services are deployable, observable, and resilient at scale. Implement and evolve deployment methodologies such as GitOps, infrastructure-as-code, and progressive delivery patterns. Build systems that automatically evaluate deployment health using metrics, logs, traces, and alerts to detect regressions and trigger safe rollbacks. Build systems that support agent-assisted or autonomous deployment workflows using modern AI tooling. Technologies commonly used in this environment include: Kubernetes for large-scale container orchestration and runtime infrastructure Python and FastAPI for internal services Terraform for infrastructure as code GitOps-based deployment workflows (e.g., ArgoCD, Flux, or similar systems) Buildkite for CI orchestration You may be a strong fit if you: Have worked with Kubernetes-based deployment systems at scale Have experience building or operating continuous deployment platforms Are familiar with GitOps tooling such as

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We're hiring Software Engineers for our Data Platform team to build and evolve Snowflake's real-time stream processing and data transformation platform. If you've built or researched high-throughput streaming systems or scalable transformation engines - in industry or graduate work - we want to talk. AS A SOFTWARE ENGINEER, DATA PLATFORM AT SNOWFLAKE, YOU WILL: Design and implement low-latency stream processing and in-flight data transformation systems at global scale Own correctness and performance of streaming execution (watermarks, exactly-once delivery, out-of-order handling) Build transformation primitives and operators that run reliably at cloud-scale throughput Contribute to architectural decisions for our next-generation streaming and transformation platform Write production-quality systems code and collaborate across product and engineering teams OUR IDEAL CANDIDATE WILL HAVE: BS/MS/PhD in Computer Science or related field — graduate research in streaming, distributed systems, or query/transformation engines is a strong differentiator Deep distributed systems fundamentals: fault tolerance, consistency, state management Hands-on or experience with stream processing or transformation systems (Flink, Kafka Streams, Spark Structured Streaming, or similar) Proficiency i

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems. You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment. What You’ll Do Own major full-stack product areas , driving features from design through production deployment. Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness. Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components. Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences. Deliver experiments and new features quickly , maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP. Adapt quickly —learning new technologies, frameworks, and tools as needed across the stack. Ideal Experience 5+ years of full-time engineering experience , post-graduation. Strong experience developing full-stack applications using React, TypeScript, and Python . Experience scaling or shipping products at high-growth startups . Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome). Proficiency with SQL and modern API development. Experience with Kubernetes , containerization, and microservice architectures. Experience working with at leas

About the Team The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers. About the Role The Storage team is building and operating a high-performance, scalable, and reliable data abstraction layer that optimizes both efficiency and reliability. Our goal is to create a platform that manages itself and fades into the background—empowering engineers to focus on delivering product experiences our customers love. This role is available across two teams within Storage, each solving unique and high-impact challenges: One team is building the orchestration layer for DoorDash’s storage platform—unifying lifecycle management, operations, and self-serve APIs for databases and streaming systems, turning complex, stateful infrastructure into reliable, developer-friendly services used across the company. One team builds and operates the distributed data platform powering DoorDash's largest stateful workloads -- including Cassandra, which backs critical product surfaces across DoorDash, Wolt, and Roo. You'll design high-throughput data abstractions, smart clients, and platform services that make distributed data reliable and easy to work with at multi-petabyte, multi-million-QPS scale, with opportunities to go deep on distributed systems internals and contribute to the open-source Cassandra ecosystem. If you're passionate about distributed systems, developer experience, and building foundational infrastructure at scale, we'd love to hear from you. You must be located in San Francisco, Sunnyvale, Seattle, or the New York Metro Area for this hybrid pos

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. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G