Pluralis Research

4 months ago

Machine Learning Engineer - ML Training Platform

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

We are seeking a Machine Learning Training Platform Engineer to design, build, and scale the foundational infrastructure for a decentralized ML training platform. Own multi-cloud resource orchestration, distributed compute, and services integration to enable continuous experimentation and large-scale model training across AWS, GCP, and Azure. You’ll work on GPU clusters, real-world networking simulations, and resilient data workflows in a fast-paced startup environment.

Location: Melbourne

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Multi-Cloud Infrastructure: Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code. Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes.
•Distributed Training Systems: Architect fault-tolerant infrastructure for distributed ML, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.
•Real-World Networking: Build systems that simulate real-world network conditions and manage dynamic node churn to ensure efficient data flow across workers with heterogeneous connectivity.
•What you’ll bring: Infrastructure & Platform Engineering with IaC, multi-cloud deployments, Docker/Kubernetes, GPU workloads, and heterogeneous clusters at scale.
•Systems Programming & Reliability: Python engineering with observability, SRE practices, monitoring, performance profiling, and incident response.

Key Requirements

•5+ years of experience in infrastructure & platform engineering with multi-cloud deployments using infrastructure-as-code (Pulumi/Terraform/CloudFormation); production experience with Docker/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale.
•Deep understanding of distributed training workflows, checkpointing, data sharding, model versioning, long-running job orchestration, and decentralized networking (P2P, NAT traversal, traffic shaping).
•Strong Python engineering (asyncio, concurrency, retry logic, cloud SDKs, CLI tooling) with hands-on observability, SRE practices, monitoring (Prometheus/Grafana), performance profiling, and incident response.
•Experience in a startup environment with emphasis on micro-services orchestration or big tech background.
•Deep understanding of multi-cloud infra & distributed training systems.

Experience:5+ yearsDistributed systemsMulti-cloudAIML infrastructure

Skills:PythonAsyncioDockerKubernetesEKSPulumiTerraformCloudFormationPrometheusGrafanaGPUNVIDIAS3Data shardingCheckpointingModel versioning

Tech Stack:PythonAWSGCPAzureDockerKubernetesEKSPulumiTerraformCloudFormationPrometheusGrafanaNVIDIAGPUS3

Company Brief

Pluralis Research

Develops Protocol Learning for decentralized, multi‑participant training of foundation models so models remain unmaterialized and community‑owned, enabling open-source large‑scale AI without single‑party control.

Industry: AI & Machine Learning

Company Size: Micro (1 to 10 employees)

Growth: Early Stage Startup

Funding: Seed

Founded: 2024

Glassdoor: 4.0

Website LinkedIn

Loading job details...

Preparing the role view and application actions.

Pluralis Research

4 months ago

Machine Learning Engineer - ML Training Platform

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Location: Melbourne

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Multi-Cloud Infrastructure: Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code. Handle dynamic scaling, state synchronization, and concurrent operations across hundreds of heterogeneous nodes.
•Distributed Training Systems: Architect fault-tolerant infrastructure for distributed ML, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.
•Real-World Networking: Build systems that simulate real-world network conditions and manage dynamic node churn to ensure efficient data flow across workers with heterogeneous connectivity.
•What you’ll bring: Infrastructure & Platform Engineering with IaC, multi-cloud deployments, Docker/Kubernetes, GPU workloads, and heterogeneous clusters at scale.
•Systems Programming & Reliability: Python engineering with observability, SRE practices, monitoring, performance profiling, and incident response.

Key Requirements

•5+ years of experience in infrastructure & platform engineering with multi-cloud deployments using infrastructure-as-code (Pulumi/Terraform/CloudFormation); production experience with Docker/Kubernetes (EKS), GPU workloads, and heterogeneous clusters at scale.
•Deep understanding of distributed training workflows, checkpointing, data sharding, model versioning, long-running job orchestration, and decentralized networking (P2P, NAT traversal, traffic shaping).
•Strong Python engineering (asyncio, concurrency, retry logic, cloud SDKs, CLI tooling) with hands-on observability, SRE practices, monitoring (Prometheus/Grafana), performance profiling, and incident response.
•Experience in a startup environment with emphasis on micro-services orchestration or big tech background.
•Deep understanding of multi-cloud infra & distributed training systems.

Experience:5+ yearsDistributed systemsMulti-cloudAIML infrastructure

Skills:PythonAsyncioDockerKubernetesEKSPulumiTerraformCloudFormationPrometheusGrafanaGPUNVIDIAS3Data shardingCheckpointingModel versioning

Tech Stack:PythonAWSGCPAzureDockerKubernetesEKSPulumiTerraformCloudFormationPrometheusGrafanaNVIDIAGPUS3

Company Brief

Pluralis Research

Industry: AI & Machine Learning

Company Size: Micro (1 to 10 employees)

Growth: Early Stage Startup

Funding: Seed

Founded: 2024

Glassdoor: 4.0

Website LinkedIn