Member of Technical Staff, Cloud Infrastructure

About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI. The Role: As a Software Engineer on our Cloud Infrastructure team, you'll be at the forefront, architecting and building the foundational systems that power Fireworks AI's revolutionary generative AI platform. You'll spearhead the creation of one of the world's first virtual clouds, seamlessly serving AI workloads across the globe and every cloud provider. Your mission: to deliver unparalleled reliability, efficiency, and scalability, fueling the world's most innovative AI products.This is a highly technical role requiring deep expertise in distributed systems, cloud-native infrastructure, and machine learning platforms. You’ll partner closely with engineering partners, product teams, and infrastructure stakeholders to design solutions that balance performance, cost-efficiency, and operational simplicity across compute, storage, and networking layers. Key Responsibilities: Architect and build scalable, resilient, and high-performance backend infrastructure to support distributed training, inference, and data processing pipelines. Lead technical design discussions, mentor other engineers, and establish best practices for building and operating large-scale ML infrastructure. Design and implement core backend services (e.g., job schedulers, resource managers, autoscalers, model serving layers) with a focus on efficiency and low latency. Drive infrastructure optimization initiatives, including compute cost reduction, storage lifecycle management, and network performance tuning. Collaborate cross-functionally with ML, DevOps, and product teams to translate research and product needs into robust infrastructure solutions. Continuously evaluate and integrate cloud-native and open-source technologies (e.g., Kubernetes, Kubeflow, MLFlow) to enhance our platform’s capabilities and reliability. Own end-to-end systems from design to deployment and observability, with a strong emphasis on reliability, fault tolerance, and operational excellence. Ensuring System Reliability: Ensure systems are designed and implemented with high availability, scalability, and performance. Focus on fault tolerance, disaster recovery, identifying and removing scaling bottlenecks, and performance optimization across our multi-cloud infrastructure. Observability & Monitoring: Develop, implement, and maintain comprehensive monitoring, alerting, logging, and tracing solutions to provide deep insights into system health and performance Minimum qualifications: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience). 5+ years of experience designing and building backend infrastructure in cloud environments (e.g., AWS, GCP, Azure). Proven experience in ML infrastructure and tooling (e.g., PyTorch, TensorFlow, Vertex AI, SageMaker, Kubernetes, etc.). Strong software development skills in languages like Python, or C++. Deep understanding of distributed systems fundamentals: scheduling, orchestration, storage, networking, and compute optimization. Preferred qualifications: Master’s or PhD in Computer Science or 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 (e.g., Terraform, ArgoCD, GitOps). Track record of driving system performance, reliability, and cost-ef