The Role:

As a Software Engineer on our AI Infrastructure team, you will help design the core systems that power Fireworks AI’s generative AI platform. You will help build infrastructure and tools that ensure the reliability, performance, quality, and availability of our AI system. 

Our mission is to make Fireworks AI the most reliable and user friendly generative AI platform in the world. You will partner closely with our cloud infrastructure team, product team, and performance team to deliver infrastructure that bridges the gap between our customer and the ultra-performant proprietary Fireworks inference engine. 

Key Responsibilities:

• Contribute to the design and development of scalable backend infrastructure that supports distributed training, inference, and data pipelines
• Build and maintain core backend services such as LLM CI/CD pipeline, control plane, and model serving systems
• Support performance optimization, cost efficiency, and reliability improvements across compute, storage, and networking layers
• Building frameworks and safeguards to ensure Fireworks AI has the best model quality in the industry
• Collaborate with performance, training, and product teams to translate research and product needs into infrastructure solutions
• Participate in code reviews, technical discussions, and continuous integration and deployment processes

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems
• Strong programming skills in Python, Go, or a similar language
• Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes, etc.).
• Basic understanding of LLM knowledge (e.g., context length, disaggregated prefill, KV cache memory estimation, etc) 

Preferred Qualifications:

• 5+ years of experience in software engineering, with a focus on infrastructure or machine learning systems
•  Experience with open source inference engine like vLLM, Sglang, or TRT-LLM
•  Contributions to open-source infrastructure or ML projects
•  Experience in building large scale ML/MLOps infrastructure