Member of Technical Staff, Cloud Infrastructure

Fireworks • San Mateo, CA

19h

About The Position

As a Software Engineer on Fireworks AI’s Cloud Infrastructure team, this role architects and builds foundational systems for its generative AI platform, including a virtual cloud serving AI workloads across cloud providers worldwide. The position focuses on delivering reliability, efficiency, and scalability across compute, storage, and networking. It requires deep expertise in distributed systems, cloud-native infrastructure, and machine learning platforms, and involves partnering with engineering, product, and infrastructure stakeholders to balance performance, cost-efficiency, and operational simplicity.

The engineer owns systems from design through deployment and observability, with emphasis on reliability, fault tolerance, high availability, scalability, performance, disaster recovery, and operational excellence. The role also leads technical design discussions, mentors engineers, and develops monitoring and observability solutions for system health and performance.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• At least 5 years of experience designing and building backend infrastructure in cloud environments such as AWS, GCP, or Azure.
• Proven experience with machine-learning infrastructure and tooling, such as PyTorch, TensorFlow, Vertex AI, SageMaker, or Kubernetes.
• Strong software development skills in languages such as Python or C++.
• Deep understanding of distributed-systems fundamentals, including scheduling, orchestration, storage, networking, and compute optimization.

Nice To Haves

• Master’s or PhD in Computer Science or a related field.
• Experience leading infrastructure projects supporting large-scale ML/AI workloads or high-throughput systems.
• Familiarity with infrastructure-as-code and CI/CD tooling such as Terraform, ArgoCD, or GitOps.
• Track record of improving system performance, reliability, and cost-efficiency.
• Contributions to open-source cloud or ML infrastructure projects.

Responsibilities

• Architect and build scalable, resilient, high-performance backend infrastructure for distributed training, inference, and data-processing pipelines.
• Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale machine-learning infrastructure.
• Design and implement backend services such as job schedulers, resource managers, autoscalers, and model-serving layers, emphasizing efficiency and low latency.
• Drive infrastructure optimization, including compute cost reduction, storage lifecycle management, and network performance tuning.
• Collaborate with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions.
• Evaluate and integrate cloud-native and open-source technologies to improve platform capabilities and reliability.
• Own systems end to end, from design and deployment to observability, reliability, fault tolerance, and operational excellence.
• Ensure systems provide high availability, scalability, and performance; address fault tolerance, disaster recovery, scaling bottlenecks, and performance optimization across multi-cloud infrastructure.
• Develop, implement, and maintain monitoring, alerting, logging, and tracing solutions to provide insight into system health and performance.

What This Job Offers

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