Senior Training Infrastructure Engineer – Scalable AI

FDE Times; San Mateo; Vor Ort; PHP 900.000 - 1.500.000; Vollzeit

Zusammenfassung

Fireworks AI is hiring a Training Infrastructure Engineer to design and build the backbone of our large-scale model training systems. You will create robust pipelines, optimize distributed workloads, and ensure reliable development across multiple GPUs and data centers.

You will collaborate with AI researchers and engineers to implement training methodologies, monitor performance, and automate provisioning.

Qualifikationen

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

Aufgaben

• 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.

Kenntnisse

Distributed systems

ML infrastructure

PyTorch

Cloud platforms

Kubernetes/Docker

Data/model parallelism

Ausbildung

Bachelor's degree in CS/CE or equivalent

Tools

Docker

Kubernetes

Cloud platforms (AWS/GCP/Azure)

Jobbeschreibung

Fireworks AI is hiring a Training Infrastructure Engineer to design and build the backbone of our large-scale model training systems. You will create robust pipelines, optimize distributed workloads, and ensure reliable development across multiple GPUs and data centers.

You will collaborate with AI researchers and engineers to implement training methodologies, monitor performance, and automate provisioning.