Machine Learning Engineer - ML Training Platform

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights ([tech report]( While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges.

For the mission in full, read [A Third Path:
Protocol Learning]

Our training and inference doesn't happen in a datacenter. It happens on consumer nodes and cloud instances that are not co-located, connected by ordinary internet, joining and leaving mid-run. Your primary role is to architect, build, and scale the platform that keeps continuous experimentation and large-scale training running on top of that: infrastructure orchestration, distributed compute, and the services that tie them together.

• Multi-cloud infrastructure :
  Design the resource management systems that provision and orchestrate compute across AWS, GCP, and Azure with infrastructure-as-code (Pulumi/Terraform). Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes.
• Distributed training and inference 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 the systems that simulate and handle real network conditions such as bandwidth shaping, latency injection, packet loss. Managing node churn and keeping data flowing across workers with heterogeneous connectivity.

• Infrastructure and platform engineering (required) :
  Production experience with infrastructure-as-code (Pulumi/Terraform/Cloud Formation) managing multi-cloud deployments, Docker/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale.
• Distributed systems and ML infrastructure :
  You understand distributed training workflows: checkpointing, data sharding, model versioning, long-running job orchestration.
• Decentralized networking : P2P, NAT traversal, traffic shaping, real bandwidth constraints.
• Systems programming and reliability :
  Strong Python engineering (asyncio, concurrency, retry logic, cloud SDKs, CLI tooling) with hands-on observability and SRE practice;
  Prometheus/Grafana, performance profiling, incident response.
• Environment fit :
  You've done this in a startup with heavy service orchestration, or at big-tech scale, and you can show which systems you owned.
• Mission alignment :
  You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

• Experience with foundation model pre-training, post-training, or RL.
• Experience at proprietary, open-weight and open-source AI labs

• Equity-Heavy Package :
  We offer significant ownership for key technical contributors in addition to a high base salary.
• Remote-First Culture :
  Flexible work environment with team members distributed globally.
• Visa Sponsorship :
  Optional full visa sponsorship and relocation support to either Australia or the US.
• Open Problems :
  Training and serving frontier models on hardware you don't control, over networks you don't own, mostly has no published answers yet. You'll write some of the first ones.

• We work remotely across the world, with the main teams in Australia and North America. You'll need to be comfortable working across timezones.
• Applicants must have professional-level English proficiency (written and spoken).

We are backed by [ Union Square Ventures ]( and other tier-1 investors, and we are a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We believe AI, and the world, end up on a better path if we succeed in implementing the protocol for intelligence.