Machine Learning Engineer - ML Training Platform

Pluralis Research develops Protocol Learning for training and serving large models in a decentralized manner across consumer devices and cloud instances. The Machine Learning Engineer will architect, build, and scale the platform supporting continuous experimentation and large-scale distributed training through infrastructure orchestration, distributed compute, and supporting services.

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

Requirements:

• Production experience with infrastructure-as-code (Pulumi/Terraform/CloudFormation) managing multi-cloud deployments, Docker/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale
• You understand distributed training workflows: checkpointing, data sharding, model versioning, long-running job orchestration
• P2P, NAT traversal, traffic shaping, real bandwidth constraints
• Strong Python engineering (asyncio, concurrency, retry logic, cloud SDKs, CLI tooling) with hands-on observability and SRE practice; Prometheus/Grafana, performance profiling, incident response
• You've done this in a startup with heavy service orchestration, or at big-tech scale, and you can show which systems you owned
• You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI
• You'll need to be comfortable working across timezones
• Applicants must have professional-level English proficiency (written and spoken)
• Experience with foundation model pre-training, post-training, or RL
• Experience at proprietary, open-weight and open-source AI labs