[Hiring] Machine Learning Engineer @Pluralis Research

Role Description

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. 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.

Key Responsibilities

• 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.

Qualifications

• 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.

Nice to Have

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

Benefits

• 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.

FYI's

• 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.