Software Engineer Distributed Systems Jobs

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, networking, load balancing (including our public-facing edge and internal service mesh), and observability and alerting systems. The Deployments team designs and maintains our continuous delivery infrastructure, ensuring reliable code deployment from development through production for all engineering teams. This infrastructure is primarily composed of Argo Workflows and ArgoCD. The team also provides tooling that enables clear system ownership and facilitates self-service onboarding for development teams. We are looking to speak to candidates who can work East Coast hours. The ideal candidate should Have 6+ years of experience in software development and operating distributed systems Proficiency in Python, Go, or a similar language Proven experience building and operating large-scale continuous integration and continuous deployment (CI/CD) pipelines Possess a customer-focused mindset Value efficiency in processes and operations Prefer automation over manual process (“allergic to ops work”). We are a small team of software engineers with a strong bias towards software solutions to avoid toil Experience using and extending containerization technologies, particularly Kubernetes, to enhance application agility, optimize resource utilization, and accelerate time-to-market Expertise in cloud infrastructure platforms, including AWS, Google Cloud Platform (GCP), or Azure Understanding of Linux operating system internals and networking concepts (e.g., TCP/IP, DNS, TLS, routing) Expectations Contribute to developing a world-class continuous deployment experience, enabling the rapid and reliable shipment of MongoDB products This includes, but is not limited to, contributing to open-source projects, or engineering software-based

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training,

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training,

Senior: PLN 260,400 - 352,200 Staff PLN 350,700 - 474,400 Subject to alignment to the responsibilities and duties of the role - we currently have multiple positions available at both Senior and Staff level. About the job Build the quality foundations for Kubernetes systems that power next-generation AI compute. As a Staff QA Engineer, you will validate the workload management stack for new AI accelerator hardware. You will help ensure our Kubernetes integrations are robust, scalable and ready for production. Your work will shape how accelerators are scheduled, orchestrated and used across distributed environments. You will test the systems that make complex AI compute infrastructure reliable for real users. You will design automated test suites, build system and integration tests, and improve CI/CD quality practices. You will also debug issues across Kubernetes, infrastructure and applications. This role is for someone who wants to raise engineering quality across a deeply technical platform. The team and culture You will join the Workload Management team, working closely with software engineers and infrastructure specialists. The work is hands-on, systems-focused and driven by clear technical ownership. Quality is built early here, not checked at the end. You will influence test strategy, validation plans and automation practices across the development lifecycle. The team moves through open technical discussion and evidence-based decisions. You will mentor others, speak up on risk and help turn complex systems into dependable products. What we’re looking for Strong Python programming experience, with a focus on automation and test engineering Hands-on experience with Robot Framework or similar automation frameworks Experience testing cloud-native, containerised or distributed systems Strong Linux understanding and confidence debugging across multiple system layers Experience with CI/CD systems such as GitLab CI or GitHub Actions Ability to define quality standards,

Karya is building the data infrastructure for the next generation of AI. We work across languages and real-world contexts to create high-quality datasets and evaluations, while creating meaningful digital work for underserved communities. We’re looking for a Senior Backend Engineer to take ownership of important parts of our platform and help shape the next generation of Karya’s engineering infrastructure. This is a role for someone who enjoys being given a difficult problem and figuring out how to solve it. You’ll work closely with the Head of Engineering and have significant autonomy over the systems you own, from architecture and implementation through production and iteration. What You'll Do Much of the infrastructure we build has constraints that are uncommon in conventional software systems. Karya operates across multiple geographies, with different requirements, environments, and network conditions. Our systems need to work reliably even when connectivity is limited or intermittent, while still supporting a centralized platform that can scale across countries without becoming a collection of country-specific systems. You’ll work on problems such as: Building backend infrastructure that can operate reliably across very different environments and geographies. Designing systems that cannot always assume a reliable network, including resilient data transfer and eventual synchronization. Evolving our existing architecture as the platform grows, making the right trade-offs between simplicity, performance, reliability, and scale. Building infrastructure and developer tooling that increases the leverage of the engineering team. Solving problems across APIs, data systems, distributed systems, integrations, performance, security, and reliability. The interesting part is that there often isn't an established pattern to follow. You’ll need to understand the constraints, make good technical decisions, and build pragmatic solutions. What You'll own You’ll own m

CSQ127R154 As a Senior Production Engineer working on Databricks’ realtime products you will be directly contributing to our customers’ success. You will build advanced monitoring and incident mitigation tooling, and drive changes across the stack to proactively make the realtime products like Lakebase, Neon and Model Serving reliable, secure, and scalable in production. Our production engineers understand the Databricks platform from end to end, and partner across engineering teams, building durable solutions for a platform operating across AWS, Azure, and GCP. The Impact You’ll Have Observability Build advanced monitoring and detection capabilities that give early warning of issues and deep insights into workload performance and customer experience. Build reliable, observable automation for debugging and incident mitigation. Reliability Improve service reliability, scalability, security, and operational efficiency. Develop dependable, safe, mitigations for production issues. On-Call & Incident Response Participate in a follow-the-sun on-call rotation and lead incident response and mitigation. Perform root-cause analysis, identify and implement lasting corrective actions. Partner with Product Engineering, Security, Support, and other infrastructure teams on follow up actions. What We Look For Experience 5+ years of experience in Production Engineering, Customer Reliability Engineering (CRE), Site Reliability Engineering (SRE), infrastructure engineering, backend software engineering, or a related field. Experience in holistic monitoring and alerting of complex stateful systems, applying multiple strategies like workload alerting, anomaly detection and probing. Experience with PostgreSQL, or related managed databases or distributed systems. Experience in incident management and participating in on-call rotations for critical infrastructure. Skillset A mindset focused on automation, root-cause resolution, and continuous improvement. Strong programming skill

JOB TITLE Senior Data Engineer, Proprietary Research A CAREER WITH POINT72’S PROPRIETARY RESEARCH On our proprietary research team—Market Intelligence—you’ll partner with our Investment Professionals and Compliance team to uncover insights about companies, industries, and the broader economy through deep fundamental research and applying data science and engineering techniques to alternative data sets. You’ll work alongside a talented team with diverse skills, backgrounds, and perspectives. Our industry researchers, product managers, and data scientists and engineers work together to build compliant research products that answer the questions our investment professionals care about most. We look for other bright, motivated, and collaborative people to join our team and grow with us—more than 90% of the leaders in our group were promoted from within. WHAT YOU'LL DO Senior Data Engineers on the Proprietary Research team design and build Data & AI Platform to transform structured and unstructured data into actionable insights. As a member of our Data Engineering team, you will work closely with investment professionals, researchers, and data scientists to design, build, and launch robust end-to-end business solutions including data pipelines, products and tools that help extract the most value out of Point72’s data assets. In this role, you will: • Design and build big data pipelines for ingestion, transformation, trend creation, and delivery of AI-ready data assets. • Develop reusable software components, libraries, and APIs to support experimentation and production deployment. • Implement data quality, lineage, and governance controls to ensure datasets are reliable, auditable, and compliant. • Build scalable, AI-native, cloud-based infrastructure using distributed systems and modern cloud technologies. • Optimize system performance, cost, and reliability through benchmarking and continuous improvement. • Explore emerging Data and AI technologies, run proof of co

We are seeking a Senior Site Reliability Engineer to join our growing Gurugram Products & Technology team to provide technical direction, shape architecture, and build key operational foundations of a new platform we are building to make it easier for customers to build AI applications using MongoDB. As a Senior Site Reliability Engineer on this new team, you will be responsible for enabling deployment at scale of AI applications and improving the performance, scalability, and reliability of the distributed systems infrastructure for this new product. The platform's SRE team owns the operational foundations: the Kubernetes fleet, networking, observability and alerting, and tenant isolation. MongoDB engineering teams pride themselves on building high-quality software and living MongoDB cultural values every day – we value intellectual curiosity and honesty, and building together in an environment that prioritizes collaboration over competition. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Position Expectations Operate and improve the multi-tenant Kubernetes infrastructure that runs customer workloads Build for reliability, making services and infrastructure available, resilient, fault-tolerant, and self-healing Identify and configure key metrics to detect incidents and quantify service health, availability, and performance Participate in a 24/7 on-call rotation to resolve issues involving platform infrastructure Mentor early-career SREs and contribute to the team’s operational practices as it grows Qualifications Strong background in software development and operating distributed systems 6+ years of experience building and operating distributed systems, with proficiency in Python, Go, or a similar programming language Experience operating Kubernetes in production and debugging below the abstraction layer, including scheduling, cluster networking, and node-level issues Expertise in cloud infrastructure platforms, in

About the Role At Together AI, you’ll build and operate one of the world’s largest GPU fleets used for frontier model training and inference. This isn’t a traditional infrastructure role—we’re looking for engineers who love building systems, automating everything, and solving problems at massive scale. If you enjoy writing software more than clicking dashboards, obsess over eliminating manual work, and want to build infrastructure that manages tens of thousands of GPUs autonomously, we’d love to talk. Responsibilities Design and build fleet automation systems that provision, validate, deploy, upgrade, repair, and retire GPU clusters with minimal human intervention. Build AI Infrastructure Agents that automate deployment, root-cause failures, incident triage, and autonomous remediation. Develop Fleet Intelligence platforms that continuously monitor hardware health, firmware, networking, storage, thermals, and workload performance to predict failures before they impact customers. Build software that maximizes GPU availability, utilization, performance, and reliability across thousands of accelerators. Create automated validation systems for GPUs, InfiniBand/RoCE fabrics, NVLink/NVSwitch, storage, and distributed AI workloads. Build internal platforms and developer tools that allow infrastructure to be managed through software—not manual operations. Continuously improve deployment velocity, reliability, and operational efficiency through automation. Partner closely with hardware, networking, platform, and AI teams to push the limits of AI infrastructure. Requirements 3+ years building distributed systems, infrastructure platforms, or large-scale backend software. Strong software engineering skills in Python, Go, or Rust . Experience building platforms, automation systems, or developer infrastructure. Experience with Linux, Kubernetes, Terraform, Ansible, or similar infrastructure technologies. Strong systems thinking with the ability to understand problems across hardw

Perplexity is looking for a Backend/Infrastructure Engineer Intern to support our small, high-impact team building the systems that power our search experience. You will work closely with experienced engineers to help design, build, and operate robust infrastructure and backend services that keep Perplexity fast, reliable, and scalable. Internship program: 12 - 24 weeks, full-time, part-time, in-person in the Belgrade office. Responsibilities: Build and maintain infrastructure that supports high-performance search systems, with mentorship from senior engineers. Design internal tools and automation to improve developer workflows and operational efficiency. Support the design, deployment, and monitoring of cloud-native systems on AWS. Contribute to improving CI/CD pipelines, testing infrastructure, and release processes. Debug and optimize Linux-based services, containers, and backend systems. Contribute to product codebases, primarily using Rust, Go or C++. Qualifications: Working towards a degree in Computer Science, Engineering, or a related field, or equivalent practical experience. Familiarity with at least one programming language such as Go, Rust, or C/C++, through coursework or projects. Basic understanding or exposure to cloud infrastructure (e.g. AWS) and Linux systems is a plus, not a requirement. Interest in infrastructure, distributed systems, or backend engineering, with willingness to learn about CI/CD, monitoring, and performance. A proactive and curious learner who enjoys collaborating in a fast-paced, innovative environment What we're looking for: You’ve built something you’re proud of: Not just a class assignment - we want to see a project you genuinely cared about and took the initiative to build and finish. You’re currently enrolled at a Serbian university: Bachelor’s, Master’s, or PhD program or you’re self-taught with a strong passion for software engineering and technology. You’re curious, persistent, and self-driven: you take ownership, work t

Senior Backend Engineer (Python / Go) Overview We are seeking a skilled and passionate Senior Backend Engineer to join our team, focusing on the design, development, and maintenance of high-performance, scalable microservices. The ideal candidate will have strong expertise in Go (Golang), and a deep understanding of distributed systems architecture. Essential Qualifications Technical Skills Skill Category Requirement Proficiency Level Programming Languages Python (Deep expertise with frameworks like FastAPI, Flask, or Django) Advanced Programming Languages Go (Golang) (Experience building robust, high-concurrency microservices) Expert Microservices / Architecture Designing, developing, and deploying RESTful APIs Expert Security Keycloak, API authentication, JWT Advanced Databases Realtime analytics (Clickhouse) Advanced Databases Relational databases (MySQL) including complex query optimization Advanced Databases NoSQL databases (Redis) Advanced Containerization / Orchestration Kubernetes for deployment and scaling Advanced Containerization / Orchestration Docker container build Advanced Testing Writing comprehensive unit, integration, and end-to-end tests Advanced Core Experience 7+ years of professional experience in backend software development, with a significant focus on building and operating microservices in a production environment. Proven ability to work with and contribute to large-scale, distributed systems. Experience with cloud platforms (AWS, Azure, or GCP) for deployment, monitoring, and scaling. Desired Qualifications Process & Tools CI/CD: Experience setting up and maintaining automated deployment pipelines Observability: Proficiency with monitoring and logging tools Source Control: Expertise in Git and collaborative workflows Architecture & Design Familiarity with event-driven architectures and streaming data processing Experience with security best practices in API design (e.g., OAuth 2.0, JWT, input validation) Knowledge of perform

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media, telecommunications, and government to build production AI agents that automate complex workflows, reason over enterprise knowledge, and operate safely at scale. The Opportunity The hardest part of enterprise AI isn't building a great model—it's building AI systems that reliably operate inside complex production environments. As a Frontier Agent Engineer (Forward Deployed Engineering) , you'll work directly with strategic enterprise customers to architect, integrate, deploy, and operate production AI systems. You'll combine modern software engineering, distributed systems, cloud infrastructure, and frontier AI technologies to transform cutting-edge models into reliable enterprise software. Unlike traditional infrastructure or platform roles, you'll work at the intersection of AI and enterprise engineering. You'll design production agent architectures, integrate with customer systems, deploy AI into mission-critical workflows, and help shape the engineering best practices for the next generation of enterprise AI. If you enjoy solving difficult systems problems, rapidly prototyping new AI capabilities, and bringing frontier AI into production, you'll fit right in. What You'll Build Enterprise AI Systems Architect and deploy production AI systems that integrate seamlessly into complex enterprise environments, including cloud platforms, data warehouses, internal APIs, business applications, and proprietary software systems. Design scalable agent architectures that combine LLMs, retrieval, memory, tools, structured knowledge, and enterprise data into reliable production workflows. B

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. As a Machine Learning Engineer on our team, you will be at the forefront of developing high-quality, production-ready models that play a central role in our decision-making processes. Your contributions will be instrumental in shaping our financial landscape. If you have a strong interest in machine learning and enjoy challenging work, Affirm is the place for you! What you'll do Use Affirm’s proprietary and other third party data to develop machine learning models that predict the likelihood of default and make an approval or decline decision to achieve business objectives Partner with platform and product engineering teams to build model training, decisioning, and monitoring systems Research ground breaking solutions and develop prototypes that drive the future of credit decisioning at Affirm Implement and scale data pipelines, new features, and algorithms that are essential to our production models Collaborate with the engineering, credit, and product teams to define requirements for new products What we look for 2+ years of experience as a machine learning engineer or PhD in a relevant field Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration. Domain knowledge in credit risk is a plus Strong engineering skills in Python and data manipulation skills like SQL Experience using large scale distributed systems like Spark or Ray Experience using open source projects and software such as scikit-learn, pandas, NumPy, XGBoost, Kubeflow Experience developing machine learning models at scale from inception to business impact Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams The ability to

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role We are seeking a Senior Software Developer to join the Elasticsearch Security team. In this role, you will contribute to the architecture and design of Elasticsearch's main features. These features include authentication, authorization, and tenant isolation. You will work with engineering and product leaders at Elastic. Your goal is to participate in providing high-performance security at all levels. This includes ensuring that our distributed data store has top-quality security at scale. What You Will Be Doing Contributing to core security initiatives from architecture to production, focusing on the delivery of new critical features. Participating in the technical design, planning, and execution for major security components inside the Elasticsearch core engine. Developing the foundational security models for features. Optimizing security performance at scale in distributed systems environments. Applying cryptographic solutions to address genuine customer use cases. Ensuring robust data isolation within shared infrastructure supporting disparate customers. Monitoring and applying the latest advancements and best practices in security. This includes authentication, identity management, cryptography, and data access management. Collaborating with peers across the company to embed security into new customer features from the outset