Machine Learning Engineer - ML Training Platform

• Infrastructure and platform engineering (required): Production experience with infrastructure-as-code (Pulumi/Terraform/CloudFormation) 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

• 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).
• Recruiters: we aren't looking for agency support at this time. We'll reach out if we need help.

We are backed by Union Square Ventures https://www.usv.com/ 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. If this resonates, please apply.

• 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.

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 https://arxiv.org/abs/2607.13332).

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 https://pluralis.ai/blog/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.