Machine Learning Senior Platform & Infrastructure Engineer

WorkGenius Group; Los Angeles (CA); On-site; USD 117,000 - 186,000; Full time

Job summary

WorkGenius Group is seeking a Machine Learning Senior Platform & Infrastructure Engineer to build and operate scalable GPU clusters and ML training platforms in Los Angeles. You will design game-simulation infrastructure for parallel rollouts, data collection, training, and evaluation.

The role involves developing CI/CD pipelines, IaC, and internal tools across cloud environments, with focus on reliability, performance, and cost efficiency.

Qualifications

• Bachelor's degree in Computer Science or related field, or equivalent practical experience, with 3+ years of software engineering experience.
• Production experience operating distributed systems and high-scale infrastructure, including Kubernetes, AWS or GCP, IaC and CI/CD.
• Experience with GPU infrastructure, including scheduling, multi-node orchestration, and optimization for long ML workloads.
• Strong Python skills and understanding of networking, microservices, and distributed systems.
• Familiarity with MLOps practices such as model versioning, pipeline orchestration, experiment tracking, and reproducible ML workflows.

Responsibilities

• Build and operate Kubernetes, multi-node GPU clusters, networking infrastructure, and distributed ML training platforms.
• Design and scale game simulation infrastructure for parallel rollouts, data collection, training, and evaluation.
• Develop CI/CD pipelines, deployment automation, artifact management, infrastructure-as-code, and internal developer tools across cloud environments.
• Improve reliability, scalability, performance, cost efficiency, observability, reproducibility, auditability, and SLO-based operations.
• Support MLOps, security governance, incident response, root-cause remediation, and mentoring of platform engineering talent.

Skills

Kubernetes

GPU infrastructure

Distributed systems

MLOps

Python

AWS

GCP

Infrastructure as code

CI/CD

Distributed ML training

Education

Bachelor’s degree in Computer Science or related field

Tools

Terraform

Job description

Title: Machine Learning Senior Platform & Infrastructure Engineer

Industry: Gaming

Location: Los Angeles, CA

Duration: 12 months

Responsibilities

• Build and operate Kubernetes, multi-node GPU clusters, networking infrastructure, and distributed ML training platforms.
• Design and scale game simulation infrastructure for parallel rollouts, data collection, training, and evaluation.
• Develop CI/CD pipelines, deployment automation, artifact management, infrastructure-as-code, and internal developer tools across cloud environments.
• Improve reliability, scalability, performance, cost efficiency, observability, reproducibility, auditability, and SLO-based operations.
• Support MLOps, security governance, incident response, root-cause remediation, and the mentoring and development of platform engineering talent.

Requirements

• Bachelor’s degree in Computer Science or a related field, or equivalent practical experience, with 3+ years of software engineering experience.
• Production experience operating distributed systems and reliable, high-scale infrastructure, including Kubernetes, AWS or GCP, infrastructure-as-code, and CI/CD.
• Experience with GPU infrastructure, including scheduling, multi-node orchestration, and resource optimization for long-running ML workloads.
• Strong Python skills and understanding of networking, microservices, infrastructure services, and distributed systems.
• Familiarity with MLOps practices such as model versioning, pipeline orchestration, experiment tracking, artifact management, and reproducible ML workflows; experience with distributed training, HPC, inference serving, high-performance networking, or game simulation is a plus.

Skills

• Kubernetes
• GPU infrastructure
• Distributed systems
• MLOps
• Python
• AWS
• GCP
• Infrastructure as code
• CI/CD
• Distributed ML training

Hourly rate is commensurate with experience and is an estimated range provided by WorkGenius.