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, 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.

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-efficiency improvements.
• Contributions to open-source cloud or ML infrastructure projects a plus.

Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.

Base Pay Range (Plus Equity): $175,000 USD - $220,000 USD

Why Fireworks AI?

• 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.

Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.