Member of Technical Staff, AI Training Infrastructure

Fireworks • San Mateo, CA

21h

About The Position

As a Training Infrastructure Engineer, you will design, build, and optimize the infrastructure that powers large-scale model training operations. Your work will support the development of high-performance AI training infrastructure. You will collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.

Fireworks is a platform for specialized intelligence that enables companies to build, train, and serve AI models tailored to their data, workflows, and products. The company provides production AI capabilities across text, image, embedding, audio, and multimodal workloads. Fireworks is a Series D company valued at $17.5 billion.

Fireworks describes its culture as collaborative and ambitious, with opportunities to work on challenging AI infrastructure problems, use bleeding-edge technology, take ownership, and collaborate with engineers and AI researchers. The company is an equal-opportunity employer committed to creating an inclusive environment for innovators.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.
• At least 3 years of experience with distributed systems and ML infrastructure.
• Experience with PyTorch.
• Proficiency in cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization and orchestration, including Kubernetes and Docker.
• Knowledge of distributed training techniques such as data parallelism, model parallelism, and FSDP.

Nice To Haves

• Master's or PhD in Computer Science or a related field.
• Experience training large language models or multimodal AI systems.
• Experience with ML workflow orchestration tools.
• Background in optimizing high-performance distributed computing systems.
• Familiarity with ML DevOps practices.
• Contributions to open-source ML infrastructure or related projects.

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 the efficiency, scalability, and cost-effectiveness of training systems.
• Troubleshoot complex performance issues in distributed training environments.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level