Machine Learning Engineer – ML Training Platform

Job not on LinkedIn

🔥 0 minutes ago

🌐 United States, Australia – Remote

🇺🇸 United States – Remote 🇦🇺 Australia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Pluralis Research

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

🌐 Web 3

Artificial Intelligence • Web 3

Pluralis Research is a foundational AI research lab focused on Protocol Learning — decentralized, multi‑participant training of foundation models where no single participant holds a full copy of the model. The group develops methods to enable communication‑efficient model and pipeline parallelism, unextractable collaborative models, and high‑compression context parallelism so community‑trained, community‑owned frontier models can scale over low‑bandwidth, internet‑connected devices. Their work targets practical systems and algorithms that make decentralized training competitive with centralized training while enabling new ownership and economic models.

📋 Description

• Architect, build, and scale the platform supporting continuous experimentation and large-scale training on consumer nodes and cloud instances • Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using Pulumi/Terraform • Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes • Architect fault-tolerant distributed ML infrastructure for GPU clusters and NVIDIA runtime • Implement S3 checkpointing, large-dataset management and streaming, health monitoring, and resilient retry strategies • Build systems simulating and handling bandwidth shaping, latency injection, and packet loss • Manage node churn and maintain data flow across workers with heterogeneous connectivity

🎯 Requirements

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

🏖️ Benefits

• Significant equity/ownership for key technical contributors in addition to a high base salary • Flexible work environment with team members distributed globally • Optional full visa sponsorship and relocation support to either Australia or the US • Opportunity to work on novel, unpublished problems in training and serving frontier models