Member of Technical Staff, Cloud Infrastructure

Fireworks AI; San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Fireworks AI in San Mateo, CA is seeking an Architect to design and build scalable backend infrastructure supporting distributed training, inference, and data processing pipelines for a generative AI platform. You will lead technical design for core services such as job schedulers and autoscalers, and ensure reliability, observability, and solid multi-cloud orchestration across environments.

Ideal candidates have a BS in CS and 5+ years in cloud backend infra and ML tooling, with deep

Qualifications

• Bachelor's degree in Computer Science or related field with 5+ years in cloud backend infrastructure and ML tooling.

Responsibilities

• Architect and build scalable backend infrastructure for distributed training, inference, and data processing pipelines.
• Lead technical design for core services like job schedulers and autoscalers.
• Ensure system reliability and observability across multi-cloud environments.

Skills

Distributed Systems

Cloud Infrastructure

Machine Learning Platforms

Kubernetes

Python

C++

PyTorch

TensorFlow

Infrastructure As Code

CI/CD

Resource Management

System Observability

Multi-cloud Architecture

Backend Engineering

Scalability

Fault Tolerance

Education

Bachelor's degree in Computer Science or related field

Job description

Architect and build scalable backend infrastructure to support distributed training, inference, and data processing pipelines for a generative AI platform. Lead technical design for core services like job schedulers and autoscalers while ensuring system reliability and observability across multi-cloud environments.

Requirements

Requires a Bachelor's degree in Computer Science or a related field with over 5 years of experience in cloud backend infrastructure and ML tooling. Candidates must have deep expertise in distributed systems and proficiency in Python or C++.

Key Skills

Distributed Systems, Cloud Infrastructure, Machine Learning Platforms, Kubernetes, Python, C++, PyTorch, TensorFlow, Infrastructure As Code, CI/CD, Resource Management, System Observability, Multi-cloud Architecture, Backend Engineering, Scalability, Fault Tolerance