Machine Learning Engineer - ML Training Platform

The role

You will architect, build, and scale a platform for continuous experimentation and large-scale training. This involves infrastructure orchestration, distributed compute, and services for training and serving large models on decentralized consumer-grade devices.

What you will do

• Design resource management systems to provision and orchestrate compute across AWS, GCP, and Azure using infrastructure-as-code.
• Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes.
• Architect fault-tolerant infrastructure for distributed ML including GPU clusters, NVIDIA runtime, and S3 checkpointing.
• Manage large-dataset management, streaming, health monitoring, and resilient retry strategies.
• Build systems that simulate and handle real network conditions such as bandwidth shaping, latency injection, and packet loss.
• Manage node churn and keep data flowing across workers with heterogeneous connectivity.

Required qualifications

• Production experience with infrastructure-as-code (Pulumi/Terraform/CloudFormation) managing multi-cloud deployments, Docker/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale.
• Understanding of distributed training workflows: checkpointing, data sharding, model versioning, long-running job orchestration.
• Knowledge of decentralized networking: P2P, NAT traversal, traffic shaping, real bandwidth constraints.
• Strong Python engineering (asyncio, concurrency, retry logic, cloud SDKs, CLI tooling).
• Hands-on observability and SRE practice: Prometheus/Grafana, performance profiling, incident response.
• Experience in a startup with heavy service orchestration or at big-tech scale.
• Belief that Protocol Learning is the viable third path for collective, trustless, and sovereign AI.
• Professional-level English proficiency (written and spoken).

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
• High base salary
• Remote-First Culture
• Optional full visa sponsorship
• Relocation support to either Australia or the US

Before you apply

• Must be comfortable working across timezones.
• Optional full visa sponsorship and relocation support to either Australia or the US.

About the company

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. They are backed by Union Square Ventures and other tier-1 investors.