Machine Learning Engineer - ML Training Platform

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Description

Overview Pluralis Research is pioneering Protocol Learning —a fully decentralised way to train and deploy AI models that opens this layer to individuals rather than well resourced corporates. By pooling compute from many participants, incentivising their efforts, and preventing any single party from controlling a model’s full weights, we’re creating a genuinely open, collaborative path to frontier-scale AI. We’re looking for an ML Training Platform Engineer to architect, build, and scale the foundational infrastructure powering our decentralized ML training platform. You will own core systems spanning infrastructure orchestration, distributed compute, and services integration, enabling continuous experimentation and large-scale model training. Responsibilities Multi-Cloud Infrastructure : Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code (Pulumi/Terraform). Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes. Distributed Training Systems : Architect fault-tolerant infrastructure for distributed ML. GPU clusters, NVIDIA runtime, S3 checkpointing, Large dataset management and streaming, health monitoring, and resilient retry strategies. Real-World Networking : Build systems that simulate and handle real-world network conditions — bandwidth shaping, latency injection, packet loss — while managing dynamic node churn and ensuring efficient data flow across workers with heterogeneous connectivity, because our training happens on consumer nodes and non co-located infrastructure, not in a datacenter. What You’ll Bring Ideally, you’ll have 5+ years of work experience with deep experience in: Infrastructure & Platform Engineering: Production experience with infrastructure-as-code (Pulumi/Terraform/CloudFormation) managing multi-cloud deployments, lifecycle orchestration, self-healing systems, Docker/Kubernetes (EKS), GPU workloads, an

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