{"schemaVersion":"jobsearcher.job.v1","id":"237579de559501816320c0ab","url":"https://jobsearcher.com/jobs/237579de559501816320c0ab","canonicalUrl":"https://jobsearcher.com/jobs/237579de559501816320c0ab","title":"Machine Learning Engineer - Distributed ML Systems","description":"Overview\r\nPluralis Research carries out foundational research on Protocol Learning : multi-participant training of foundation models where no single participant has, or can ever obtain, a full copy of the model. The purpose of Protocol Learning is to facilitate the creation of community-trained and community-owned frontier models with self-sustaining economics.\r\nWe're looking for Senior/Staff engineers with 5+ years of experience in distributed systems and ML large-scale training. You'll be implementing a novel substrate for training distributed ML models that work under consumer grade internet connection.\r\nResponsibilities\r\nDistributed Training Architecture & Optimization\r\nDesign and implement large-scale distributed training systems optimized for heterogeneous hardware operating under low-bandwidth, high-latency conditions.\r\nDevelop and optimize model-parallel training strategies (data, tensor, pipeline parallelism) with custom sharding techniques that minimize communication overhead.\r\nOptimize GPU utilization, memory efficiency, and compute performance across distributed nodes.\r\nImplement robust checkpointing, state synchronization, and recovery mechanisms for long-running, fault-prone training jobs.\r\nBuild monitoring and metrics systems to track training progress, model quality, and system bottlenecks.\r\nDecentralized Networking & Resilience\r\nArchitect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave.\r\nDesign and optimize peer-to-peer topologies for decentralized coordination across non-co-located nodes.\r\nImplement NAT traversal, peer discovery, dynamic routing, and connection lifecycle management.\r\nProfile and optimize communication patterns to reduce latency and bandwidth overhead in multi-participant environments.\r\nWhat You'll Bring\r\nStrong experience building and operating distributed systems in production.\r\nHands-on expertise with distributed training frameworks (FSDP, DeepSpeed, Megatron, or similar).\r\nDeep understanding of model parallelism (data, tensor, pipeline parallelism).\r\nExpert-level Python with production experience (concurrency, error handling, retry logic, clean architecture).\r\nStrong networking fundamentals: P2P systems, gRPC, routing, NAT traversal, distributed coordination.\r\nExperience optimizing GPU workloads, memory management, and large-scale compute efficiency.\r\nWhat We Offer\r\nEquity-heavy compensation with meaningful ownership in a mission-driven company\r\nCompetitive base salary for senior engineering roles in Australia\r\nVisa sponsorship available for exceptional candidates\r\nRemote-first with optional access to our Melbourne hub\r\nWorld-class team — team mates were previously at Google, Amazon, Microsoft, and leading startups\r\nBacked by Union Square Ventures and other tier-1 investors, we're a world-class, deeply technical team of ML researchers and engineers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.\r\nJ-18808-Ljbffr","company":"Pluralis Research","rawCompany":"pluralis research","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-09T09:06:24.674Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541519","title":"Other Computer Related Services","slug":"other-computer-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - Distributed ML Systems","description":"Overview\r\nPluralis Research carries out foundational research on Protocol Learning : multi-participant training of foundation models where no single participant has, or can ever obtain, a full copy of the model. The purpose of Protocol Learning is to facilitate the creation of community-trained and community-owned frontier models with self-sustaining economics.\r\nWe're looking for Senior/Staff engineers with 5+ years of experience in distributed systems and ML large-scale training. You'll be implementing a novel substrate for training distributed ML models that work under consumer grade internet connection.\r\nResponsibilities\r\nDistributed Training Architecture & Optimization\r\nDesign and implement large-scale distributed training systems optimized for heterogeneous hardware operating under low-bandwidth, high-latency conditions.\r\nDevelop and optimize model-parallel training strategies (data, tensor, pipeline parallelism) with custom sharding techniques that minimize communication overhead.\r\nOptimize GPU utilization, memory efficiency, and compute performance across distributed nodes.\r\nImplement robust checkpointing, state synchronization, and recovery mechanisms for long-running, fault-prone training jobs.\r\nBuild monitoring and metrics systems to track training progress, model quality, and system bottlenecks.\r\nDecentralized Networking & Resilience\r\nArchitect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave.\r\nDesign and optimize peer-to-peer topologies for decentralized coordination across non-co-located nodes.\r\nImplement NAT traversal, peer discovery, dynamic routing, and connection lifecycle management.\r\nProfile and optimize communication patterns to reduce latency and bandwidth overhead in multi-participant environments.\r\nWhat You'll Bring\r\nStrong experience building and operating distributed systems in production.\r\nHands-on expertise with distributed training frameworks (FSDP, DeepSpeed, Megatron, or similar).\r\nDeep understanding of model parallelism (data, tensor, pipeline parallelism).\r\nExpert-level Python with production experience (concurrency, error handling, retry logic, clean architecture).\r\nStrong networking fundamentals: P2P systems, gRPC, routing, NAT traversal, distributed coordination.\r\nExperience optimizing GPU workloads, memory management, and large-scale compute efficiency.\r\nWhat We Offer\r\nEquity-heavy compensation with meaningful ownership in a mission-driven company\r\nCompetitive base salary for senior engineering roles in Australia\r\nVisa sponsorship available for exceptional candidates\r\nRemote-first with optional access to our Melbourne hub\r\nWorld-class team — team mates were previously at Google, Amazon, Microsoft, and leading startups\r\nBacked by Union Square Ventures and other tier-1 investors, we're a world-class, deeply technical team of ML researchers and engineers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.\r\nJ-18808-Ljbffr","datePosted":"2026-04-09T09:06:24.674Z","dateModified":"2026-04-09T09:06:24.674Z","hiringOrganization":{"@type":"Organization","name":"Pluralis Research","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"237579de559501816320c0ab"},"url":"https://jobsearcher.com/jobs/237579de559501816320c0ab"}}