AI Training Infrastructure Engineer - Scale LLM Training

Fireworks AI; San Mateo (CA); On-site; USD 175,000 - 220,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Competitive salary

Job summary

Fireworks AI, based in San Mateo, CA, is seeking a Member of Technical Staff, AI Training Infrastructure to design and optimize large-scale model training systems. You will work with researchers and engineers to build robust training pipelines, scale distributed workloads, and ensure reliable model development.

The role requires hands-on experience with distributed ML infrastructure, PyTorch, and cloud platforms.

Qualifications

• Bachelor's degree in CS, CE, or related field or equivalent practical experience.
• 3+ years of experience with distributed systems and ML infrastructure.
• Experience with PyTorch.
• Proficiency in cloud platforms (AWS, GCP, Azure).
• Experience with containerization and orchestration (Kubernetes, Docker).

Responsibilities

• Design and implement scalable infrastructure for large-scale model training workloads.
• Develop and maintain distributed training pipelines for LLMs and multimodal models.
• Optimize training performance across multiple GPUs, nodes, and data centers.
• Implement monitoring, logging, and debugging tools for training operations.
• Architect and maintain data storage solutions for large-scale training datasets.
• Automate infrastructure provisioning, scaling, and orchestration for model training.
• Collaborate with researchers to implement and optimize training methodologies.
• Analyze and improve efficiency, scalability, and cost-effectiveness of training systems.
• Troubleshoot complex performance issues in distributed training environments.

Skills

Distributed systems

ML infrastructure

PyTorch

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

GCP

Azure

Kubernetes

Docker

FSDP

Job description

Fireworks AI, based in San Mateo, CA, is seeking a Member of Technical Staff, AI Training Infrastructure to design and optimize large-scale model training systems. You will work with researchers and engineers to build robust training pipelines, scale distributed workloads, and ensure reliable model development.

The role requires hands-on experience with distributed ML infrastructure, PyTorch, and cloud platforms.