Member of Technical Staff, AI Training Infrastructure
Remote • San Mateo • FullTime
Posted 1mo ago
Job Location
San Mateo
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
Fireworks is seeking a Training Infrastructure Engineer to design, build, and optimize the infrastructure that powers large-scale model training operations. This role is crucial for developing high-performance AI training infrastructure, requiring collaboration with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development. The position offers the opportunity to solve hard problems at the forefront of AI infrastructure, build what's next with bleeding-edge technology, and have a direct impact on the future of AI within a fast-growing, passionate team.
Responsibilities
- Design and implement scalable infrastructure for large-scale model training workloads
- Develop and maintain distributed training pipelines for LLMs and multimodal models
- Optimize training performance across multiple GPUs, nodes, and data centers
- Implement monitoring, logging, and debugging tools for training operations
- Architect and maintain data storage solutions for large-scale training datasets
- Automate infrastructure provisioning, scaling, and orchestration for model training
- Collaborate with researchers to implement and optimize training methodologies
- Analyze and improve efficiency, scalability, and cost-effectiveness of training systems
- Troubleshoot complex performance issues in distributed training environments
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience
- 3+ years of experience with distributed systems and ML infrastructure
- Experience with PyTorch
- Proficiency in cloud platforms (AWS, GCP, Azure)
- Experience with containerization and orchestration (Kubernetes, Docker)
- Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)
- Master's or PhD in Computer Science or related field (preferred)
- Experience training large language models or multimodal AI systems (preferred)
- Experience with ML workflow orchestration tools (preferred)
- Background in optimizing high-performance distributed computing systems (preferred)
- Familiarity with ML DevOps practices (preferred)
- Contributions to open-source ML infrastructure or related projects (preferred)
Benefits
- 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.