Software Engineer (LLM Infrastructure)

Fireworks AI; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Fireworks AI is seeking a Software Engineer for the AI Infrastructure team to design core systems powering our generative platform. You will help build infrastructure and tools for reliability, performance, and availability of the AI system.

You will partner with the cloud, product, and performance teams to deliver infrastructure that connects customers to our ultra-performant inference engine, supporting distributed training, inference, and data pipelines.

Qualifications

• 3 years of software engineering experience with infrastructure or ML systems.
• 5+ years of software engineering experience with infrastructure or machine learning systems.
• Proven experience in ML infrastructure and tooling (e.g., PyTorch, MLflow, Vertex AI, SageMaker, Kubernetes).
• Experience with open source inference engines like vLLM, Sglang, or TRT-LLM.
• Contributions to open-source infrastructure or ML projects.

Responsibilities

• Design core systems powering Fireworks AI's generative platform.
• Build infrastructure and tools ensuring reliability, performance, quality and availability.
• Collaborate with cloud, product, and performance teams to bridge customers and Fireworks engine.
• Develop scalable backend infra for distributed training, inference, and data pipelines.
• Build and maintain core backend services like LLM CI/CD, control plane, and model serving.

Skills

Python

Go

ML infra tooling

Kubernetes

PyTorch

MLflow

Vertex AI

SageMaker

Open source infra

LLM basics

Education

Bachelor's degree in Computer Science or related field

Tools

Open source inference engines

Job description

• 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
• 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
• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving
• Build What's Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI- no bureaucracy, just results
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation

• 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)
• Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• Strong programming skills in Python, Go, or a similar language
• 3 years of experience in software engineering, with a focus on infrastructure or machine learning systems
• 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
• Experience in building large scale ML/MLOps infrastructure
• Contributions to open-source infrastructure or ML projects