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About the Team The Recursive Self-Improvement (RSI) team works across research, engineering, product, and infrastructure to build AI systems that accelerate and ultimately conduct high-quality research at OpenAI. We work to automate real research workflows and improve research productivity by building systems and feedback loops, designing evaluations, and training models to develop missing capabilities. Our work spans the full lifecycle of model training, evaluation, and deployment to help researchers move faster and tackle increasingly ambitious problems. About the Role We’re hiring research scientists , research engineers , and AI systems engineers to work on automating research at OpenAI. This role is based in San Francisco, CA. In this role, you will: Design evaluations for research judgment, hypothesis generation and testing, and long-horizon experiment execution. Turn real research workflows and model failures into data and evaluation flywheels. Improve model research capabilities through agent harnesses, synthetic data, RL environments, and model training. Build and maintain safe, reliable integrations between our models and OpenAI’s research infrastructure. Develop research agents, experiment-orchestration systems, and sandboxed runtimes that support real research workflows. Create metrics and economic models to understand RSI’s current and future effects on research productivity, model capabilities, and the safety of internal deployments. This is a high-ownership role for researchers and engineers who thrive in ambiguity, move fluidly between research and implementation, and turn emerging opportunities into rigorous, reliable, scalable results. You might thrive in this role if you: Have research or engineering experience across LLM training, model evaluations, agent systems, synthetic data, research infrastructure, or large-scale distributed systems. Are a strong generalist who can move between open-ended research and practical implementation, turning ambig

About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Bengaluru, India About the role We are looking for a talented Distributed Systems Engineer to join the Data Localization team. The team builds the infrastructure that enforces where customer data is stored, processed, and decrypted across one of the largest globally distributed edge networks in the world. The problem is not simply building fast, resilient distributed systems; it is building them with provable geographic bounda

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! We’re looking for a senior engineer to help build, maintain and evolve the training framework that powers our frontier-scale language models. This role sits at the intersection of large-scale training, distributed systems, and HPC infrastructure. You will design and maintain the core components that enable fast, reliable, and scalable model training — and build the tooling that connects research ideas to thousands of GPUs. If you enjoy working across the full stack of ML systems, this role gives you the opportunity and autonomy to have massive impact. What You’ll Work On Build and own the training framework responsible for large-scale LLM training. Design distributed training abstractions (data/tensor/pipeline parallelism, FSDP/ZeRO strategies, memory management, checkpointing). Improve training throughput and stability on multi-node clusters (e.g., GB200/300, AMD, H200/100). Develop and maintain tooling for monitoring, logging, debugging, and developer ergonomics. Collaborate closely with infra teams to ensure our cluster, container environments, and hardware configurations support high-performance training. Investigate and res

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 Ray Data Team: Ray Data is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data. The Ray Data team currently develops and maintains Ray Data . We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads. As part of this role, you will: Improve the performance of Ray Data and multi-modal batch inference use cases. Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment. Building data loading solutions for production training workloads. Focus on stability and fault tolerance at high scale Working with customers and new age AI native companies in scaling their AI workloads. We'd love to hear from you if have: At least 3-4 years of relevant work experience Solid background in building scalable and fault-tolerant distributed systems Experience with data processing, database internals. Passionate about large

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 Ray Data Team: Ray Data is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data. The Ray Data team currently develops and maintains Ray Data . We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads. As part of this role, you will: Improve the performance of Ray Data and multi-modal batch inference use cases. Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment. Building data loading solutions for production training workloads. Focus on stability and fault tolerance at high scale Working with customers and new age AI native companies in scaling their AI workloads. We'd love to hear from you if have: At least 3-4 years of relevant work experience Solid background in building scalable and fault-tolerant distributed systems Experience with data processing, database internals. Passionate about large

NVIDIA is a global leader in high-speed computer vision, artificial intelligence (AI), and deep learning. Our team develops data engineering solutions that empower AI developers in autonomous vehicle (AV) domains to innovate quickly and effectively at scale. Are you ready to take on a senior technical role in building high-performance AI data pipelines? We seek an exceptional individual to design and optimize microservices and data pipelines to process massive volumes of AV data and enable seamless data mining and AI training. The ideal candidate will bring expertise in big data processing and distributed computing to create efficient solutions and overarching architectures for challenges such as video data curation, behavioral search, and AI dataset management. What you'll be doing: Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training. Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management. Deploy AI models. Design and develop Retrieval-Augmented Generation (RAG) workflows enabling hybrid and agentic patterns. Analyze and operationalize complex distributed systems for speed-of-light performance. What we need to see: Experience developing high-performance, scalable software systems. MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field. Strong programming skills in Python or Golang Proficiency in key technologies like Kubernetes, Helm, Hive, Parquet, SQL, vector databases, e.g., Milvus. Strong architectural skills with a proactive, problem-solving mentality. Experience in data mi

At Bazaarvoice, we create smart shopping experiences. Through our expansive global network, product-passionate community & enterprise technology, we connect thousands of brands and retailers with billions of consumers. Our solutions enable brands to connect with consumers and collect valuable user-generated content, at an unprecedented scale. This content achieves global reach by leveraging our extensive and ever-expanding retail, social & search syndication network. And we make it easy for brands & retailers to gain valuable business insights from real-time consumer feedback with intuitive tools and dashboards. The result is smarter shopping: loyal customers, increased sales, and improved products. The problem we are trying to solve : Brands and retailers struggle to make real connections with consumers. It's a challenge to deliver trustworthy and inspiring content in the moments that matter most during the discovery and purchase cycle. The result? Time and money spent on content that doesn't attract new consumers, convert them, or earn their long-term loyalty. Our brand promise : closing the gap between brands and consumers. Founded in 2005, Bazaarvoice is headquartered in Austin, Texas with offices in North America, Europe, Asia and Australia. It’s official: Bazaarvoice is a Great Place to Work in the US , Australia, India, Lithuania, France, Germany and the UK! Who we want: Are you ready to combine your talent for crafting solid data systems and enthusiasm for cutting-edge technology to harness the power of data at Bazaarvoice? We’re looking for a strong data engineer who thrives on building large-scale, robust, distributed data systems and pipelines, who understands the importance of good software engineering practices to get it done. If you’re excited about shaping the future of data at Bazaarvoice, come join us. How you will make an impact: As a key member of the Insights team, you'll be tasked with designing, building, and supporting large-

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 As a Distributed LLM Inference Engineer, you will help systems and optimizations that push the boundaries of performance for inference at large scale. This is an incredibly critical role to Anyscale as it allows us to achieve a market leading position for AI infrastructure. As part of this role, you will Iterate very quickly with product teams to ship the end to end solutions for Batch and Online inference at high scale which will be used by open-source Ray users and customers of Anyscale Work across the stack integrating Ray Data and LLM engine providing optimizations achieving low cost solutions for large scale ML inference Integrate with Open source software like vLLM, work closely with the community to adopt these techniques in Anyscale solutions, and also contribute improvements to open source Follow the latest state-of-the-art in the open source and the research community, implementing and extending best practices We'd love to hear from you if you have Familiarity with running ML inference at large scale with high throughput and low latency Familiarity with deep learning and deep learning frameworks (e.g. PyTorch) Solid understanding of distributed systems, ML inference challenges Bonus points

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Software Engineer to join our Data Acquisition team. Responsibilities: Own and lead engineering projects in the area of data acquisition including web crawling, data ingestion, and search. Collaborate with other sub-teams, such as Data Processing, Architecture, and Scaling, to ensure smooth data flow and system operability. Work closely with the legal team to handle any compliance or data privacy-related matters. Develop and deploy highly scalable distributed systems capable of handling petabytes of data. Architect and implement algorithms for data indexing and search capabilities. Build and maintain backend services for data storage, including work with key-value databases and synchronization. Deploy solutions in a Kubernetes Infrastructure-as-Code environment and perform routine system checks. Conduct and analyze experiments on data to provide insights into system performance. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in software development. Experience with large web crawlers a plus Strong expertise in large stateful distributed systems and data processing. Proficiency in Kubernetes, and Infrastructure-as-Code concepts. Willingness and enthusiasm for trying new approaches and technologies. Ability to handle multiple tasks and adapt to changing priorities. Strong communication skills, both written and verbal. 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

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: Ray aims to provide a universal API for building distributed applications (e.g. a machine learning pipeline of feature engineering, model training, and evaluation). Data is usually a core element connecting these different stages, and therefore plays a critical role in Ray’s usability, performance, and stability. We are looking for strong engineers to build, optimize, and scale Ray’s Datasets library and data processing capabilities in general. About the Ray Data team: The Ray Data team currently develops and maintains the Ray Datasets library, which is already powering critical production use cases (e.g. large scale data compaction at Amazon , and ML pipeline at Alibaba ). Ray Datasets is a Python library built on top of Apache Arrow and Ray Core (Ray’s C++ backend), and the Ray Data team interacts closely with Ray Core components including the scheduler and the memory & I/O subsystems. The Ray Data team also works closely with Ray’s ML libraries including Train, RLlib, and Serve. A snapshot of projects you will work on: - Performance of Ray Datasets at large scale (leveraging Arrow primitives, optimizing Ray object manager, etc.) - Integration with ML training and data sources - Stability an

Atlas Search is a multi-cloud service that allows users to execute complex full text and vector search queries using the MongoDB Query Language . Our users are free to focus on relevance and data retrieval instead of the machinery needed to search data at scale. Our team is building the cloud-based distributed systems software responsible for the lifecycle of search indexes including: data ingestion, index building, partitioning, performance, availability, and backup management. Our product is quickly gaining traction with customers and we are making core architectural improvements that you will contribute to. This role is based in Toronto, ON hybrid. Successful candidates will have the following qualities: 2+ years of hands-on experience designing, building, testing, and maintaining industrial-strength backend software in a complex codebase Experience developing distributed systems and cloud services Experience with at least one modern statically typed programming language, and interest in working with Java Excellent verbal and written technical communication skills and enthusiasm for collaborating closely with colleagues A growth mindset and the desire to learn quickly through taking on challenges, reflecting on outcomes, and incorporating feedback A strong sense of ownership over their work, from initial design all the way through maintaining code in production You will: Contribute to the design, implementation, and support of projects that improve the scalability of Atlas Search to make using it a seamless experience for even the largest workloads Work with a collaborative team that prioritizes sound technical decision-making and building systems that our customers love and that we are proud of as engineers Have the opportunity to lead projects and own subsystems Provide input on the team’s roadmap and help determine the architecture of our system Success measures: In 3 months you’ll have a solid high-level understanding of what our team does and how we operate.

Atlas Search is a multi-cloud service that allows users to execute complex full text and vector search queries using the MongoDB Query Language . Our users are free to focus on relevance and data retrieval instead of the machinery needed to search data at scale. Our team is building the cloud-based distributed systems software responsible for the lifecycle of search indexes including: data ingestion, index building, partitioning, performance, availability, and backup management. Our product is quickly gaining traction with customers and we are making core architectural improvements that you will contribute to. We are looking to speak to candidates who are based in San Francisco, CA for our hybrid working model. Successful candidates will have the following qualities: 2+ years of hands-on experience designing, building, testing, and maintaining industrial-strength backend software in a complex codebase Experience developing distributed systems and multithreaded applications Experience with at least one modern statically typed programming language, and interest in working with Java Excellent verbal and written technical communication skills and enthusiasm for collaborating closely with colleagues A growth mindset and the desire to learn quickly through taking on challenges, reflecting on outcomes, and incorporating feedback A strong sense of ownership over their work, from initial design all the way through maintaining code in production You will: Contribute to the design, implementation, and support of projects that improve the scalability of Atlas Search to make using it a seamless experience for even the largest workloads Work with a collaborative team that prioritizes sound technical decision-making and building systems that our customers love and that we are proud of as engineers Have the opportunity to lead projects and own subsystems Provide input on the team’s roadmap and help determine the architecture of our system Success measures: In 3 months you’ll have a

About the Team The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale. Our systems unify how researchers train and serve models, abstracting away the complexity of performance, parallelism, and execution across vast GPU/accelerator fleets. By providing this foundation, the Workload team ensures that researchers can focus on advancing model capabilities while we handle the scale, efficiency, and reliability required to bring those models to life. About the Role We are looking for an engineer to design and implement the dataset infrastructure that powers OpenAI’s next-generation training stack. You will be responsible for building standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively testing performance bottlenecks. In this role, you will collaborate closely with the multimodal researchers, and other infra groups to ensure datasets are unified, efficient, and easy to consume. In this role, you will: Design and maintain standardized dataset APIs, including for multimodal (MM) data that cannot fit in memory. Build proactive testing and scale validation pipelines for dataset loading at GPU scale. Collaborate with teammates to integrate datasets seamlessly into training and inference pipelines, ensuring smooth adoption and a great user experience. Document and maintain dataset interfaces so they are discoverable, consistent, and easy for other teams to adopt. Establish safeguards and validation systems to ensure datasets remain reproducible and unchanged once standardized. Debug and resolve performance bottlenecks in distributed dataset loading (e.g., straggler systems slowing global training). Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets. You might thrive in this role if you: Have strong engineering fundamentals with experience in distributed systems, data pipelines, or infrastructure.

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 are hiring a Principal Engineer II to architect the core data processing engine of the Snowflake Data & AI Cloud. At Snowflake, we believe that high-performance, unified compute fabrics are the indispensable building blocks for Agentic AI. Autonomous agents require more than just models; they require a high-fidelity, low-latency state layer to reason, act, and persist context. This role is not about building traditional data processing pipelines or legacy ETL/ELT workflows; it is about building the core distributed systems and atomic primitives that make those agentic workflows possible. In this role, you will be a lead architect of the Snowflake Data Transformation Engine. You will design and implement the fundamental transformations infrastructure—Stateful Stream Processing Engines, Incremental View Maintenance Engine, Materialization Internals, and the Distributed Orchestration Fabric. Our solid foundation supporting the seamless transition for enterprises between batch and streaming through Dynamic Tables, Streams & Tasks, and DBT Projects is the starting point. Your architectural work will extend the reach of the core engine to accelerate and support the massive scale of the Snowpark and Spark ecosystems. You are building the systems that allow both data eng

We are hiring a Senior Software Engineer to join our Server Security team. The Server Security team is a development-focused group within MongoDB's core engineering organization. Operating "close to the bottom of the stack," the team builds features that enable database users to secure their data globally. You will work on critical components including: Cryptography: Queryable Encryption , at-rest data encryption, and fundamental cryptographic principles. Identity & Access: Authentication and authorization systems, TLS, and X.509 certificate management Network Security: High-performance, low-latency networking protocols (PKI, Hashing, CRLs) System Integrity: Resilience, observability, and compliance assurance within a large-scale distributed database Our team champions a strong culture of inclusivity, diversity, and collaboration. If you want to work on a collaborative team that applies distributed systems fundamentals to deliver core features of a popular database, join us! Let’s change what’s possible for application developers, system architects, and database operators. The role As a Senior Engineer, you will apply distributed systems fundamentals to deliver core security features. You will be a leader in improving MongoDB's security posture by owning features and leading investigations into complex areas of the codebase. What you’ll do: Build and test new security features in a large, feature-rich C++ codebase Work across engineering, cloud services, and support teams to coordinate feature rollouts and changes Stand for code quality and security best practices, assisting fellow engineers in writing well-reasoned, secure code Use strong diagnostic intuition to solve thorny technical issues related to distributed systems, concurrency, and OS internals This role can be remote or hybrid anywhere in the USA or Canada. We will prioritize candidates who are already located in one of these countries. Candidate Profile We are looking for a highly technical engineer w