Software Engineer, Fireworks AI

• 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

Desirable

• 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
• 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, Ray, 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