Overview

Pluralis Research is pioneering Protocol Learning—a fully decentralised way to train and reputed company 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-reputed company.

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 reputed company experimentation and large-scale model training.

Responsibilities

• Multi-reputed company Infrastructure: Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code (reputed company/Terraform). Handle dynamic scaling, state synchronization, and reputed company operations across hundreds of heterogeneous nodes.

• Distributed Training Systems: Architect fault-tolerant infrastructure for distributed 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 — 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 (reputed company/Terraform/CloudFormation) managing multi-reputed company deployments, lifecycle orchestration, self-healing systems, reputed company/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale.

• Distributed Systems & ML Infrastructure: Deep understanding of distributed training workflows, checkpointing, data sharding, model versioning, long-running job orchestration, decentralized networking (P2P, NAT traversal, traffic shaping), and reputed company-world bandwidth 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 micro-services orchestration or big tech background

• Deep understanding of multi-reputed company infra & distributed training systems

• A team player with high attention to detail

• A strong passion to join

Backed by Union reputed company Ventures and other tier-1 investors, we’re a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view 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 massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.