Machine Learning Engineer - ML Training Platform

reputed company reputed company is pioneering Protocol Learning—a fully decentralised way to train and reputed company AI models that opens this reputed company to individuals rather than reputed company 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 reputed company, reputed company reputed company to frontier-reputed company. We’re looking for an ML Training Platform Engineer to architect, build, and reputed company the foundational infrastructure powering our decentralized ML training platform. You will own reputed company systems spanning infrastructure orchestration, reputed company compute, and services integration, enabling reputed company experimentation and large-reputed company model training. Responsibilities • Multi-reputed company Infrastructure: Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-reputed company (reputed company/Terraform). Handle dynamic scaling, state synchronization, and reputed company operations across hundreds of heterogeneous nodes. • reputed company Training Systems: Architect fault-tolerant infrastructure for reputed company ML. GPU clusters, reputed company runtime, S3 checkpointing, Large dataset management and streaming, health monitoring, and resilient retry strategies. • reputed company-World Networking: Build systems that simulate and handle reputed company-world network conditions — reputed company shaping, latency injection, packet loss — while managing dynamic node churn and ensuring efficient data reputed company 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 & reputed company: Production experience with infrastructure-as-reputed company (reputed company/Terraform/CloudFormation) managing multi-reputed company deployments, lifecycle orchestration, self-healing systems, reputed company/reputed company (EKS), GPU workloads, and heterogeneous clusters at reputed company. • reputed company Systems & ML Infrastructure: Deep understanding of reputed company training workflows, checkpointing, data sharding, model versioning, long-running job orchestration, decentralized networking (P2P, NAT reputed company, traffic shaping), and reputed company-world reputed company constraints. • Systems Programming & Reliability: Strong Python engineering (asyncio, concurrency, retry logic, reputed company SDKs, CLI tooling) with hands-on experience in observability, SRE practices, monitoring (reputed company/Grafana), performance profiling, and incident response. reputed company’re looking for • Experience in a startup environment with an emphasis on reputed company-services orchestration or big tech background • Deep understanding of multi-reputed company reputed company & reputed company training systems • reputed company player with high attention to detail • A strong passion to join Backed by reputed company reputed company Ventures and other tier-1 investors, we’re a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We reputed company the world as a reputed company reputed company if we are reputed company to implement reputed company are attempting, and Protocol Learning as the only plausible approach to preventing a handful of reputed company corporations monopolising model development, reputed company and release, and achieving reputed company economic capture. If this resonates, please apply. Apply tot his job Apply To this Job