Member of Engineering (Scalability)

ABOUT POOLSIDEIn this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.ABOUT OUR TEAMWe were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.ABOUT THE ROLEYou would be working in our pre-training team focused on building out our distributed training and inference of Large Language Models (LLMs). This is a hands-on role that focuses on software reliability and fault tolerance. You will work on cross-platform checkpointing, NCCL recovery, and hardware fault detection. You will make high-level tools. You will not be afraid of debugging Linux kernel modules. You will have access to thousands of GPUs to test changes.Strong engineering skills are a prerequisite. We assume good knowledge of Torch, NVIDIA GPU architecture, reliability concepts, distributed systems, and best coding practices. A basic understanding of LLM training and inference principles is required. We look for fast learners who are prepared for a steep learning curve and are not afraid to step out of their comfort zone.YOUR MISSIONTo help train the best foundational models for source code generation in the worldRESPONSIBILITIESIdentify, study, and troubleshoot hardware problems during training at scaleMinimize the GPU idle time during faults, both operationally and strategicallyDesign and develop tools and add-ons to accelerate the training recoveryImprove the performance and reliability of checkpointingWrite high-quality Python (PyTorch), Cython, C/C++, CUDA API codeSKILLS & EXPERIENCEUnderstanding of Large Language Models (LLM)Basic knowledge of TransformersKnowledge of deep learning fundamentalsStrong engineering backgroundProgramming experienceLinux API, Linux kernelStrong algorithmic skillsPython with numpy, PyTorch, or JaxC/C++NCCLUse modern tools and are always looking to improveStrong critical thinking and ability to question code quality policies when applicableDistributed systemsReliabilityObservabilityFault-toleranceK8s stackPROCESSIntro call with one of our Founding EngineersTechnical Interview(s) with one of our Founding EngineersTeam fit call with the People ... (truncated, view full listing at source)