Senior ML Infrastructure Engineer - Scalable GPU Training

ATOMS Careers page; San Francisco (CA); On-site; USD 224,000 - 280,000; Full time

Benefits offered by this job

Medical, Dental, Vision Insurance

401(k)

Unlimited Flexible Time Off

Job summary

ATOMS Careers page is seeking a Staff Machine Learning Infrastructure Engineer to design and implement ML training infrastructure in San Francisco. The role involves building high-performance training pipelines and managing distributed GPU workloads.

Ideal candidates should have 8+ years of software engineering experience, strong backend programming skills, and expertise in Kubernetes. The position offers a competitive salary range of $224,000 - $280,000 per year, along with comprehensive benefits.

Qualifications

• 8+ years of professional software engineering experience.
• Proficiency in backend systems programming with relevant languages.
• Hands-on experience with container orchestration using Kubernetes.

Responsibilities

• Design and build training infrastructure for ML models.
• Leverage distributed compute frameworks for ML training jobs.
• Build data ingestion pipelines for large-scale data processing.

Skills

Software engineering experience

Backend programming skills (Go, Python, Java)

Kubernetes

Distributed ML frameworks (e.g., Ray)

MLOps pipelines

High-throughput data pipelines

Job description

ATOMS Careers page is seeking a Staff Machine Learning Infrastructure Engineer to design and implement ML training infrastructure in San Francisco. The role involves building high-performance training pipelines and managing distributed GPU workloads.

Ideal candidates should have 8+ years of software engineering experience, strong backend programming skills, and expertise in Kubernetes. The position offers a competitive salary range of $224,000 - $280,000 per year, along with comprehensive benefits.