Member of Technical Staff, AI Training Infrastructure
Design and optimize infrastructure for large-scale AI model training at a leading generative AI company.
Posted by employer 3 months ago
First seen on Joblaze 3 months ago
Last verified on the company career page 1 hour ago
Requirements
- Experience
- 3+ years
- Education
- Bachelor's degree
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In the role of Training Infrastructure Engineer at Fireworks AI, the individual will focus on designing and optimizing the infrastructure necessary for large-scale AI model training. Key skills include experience with distributed systems, proficiency in PyTorch, and familiarity with cloud platforms like AWS and GCP. This position is ideal for someone with a solid background in machine learning infrastructure and at least three years of relevant experience, particularly in high-performance computing environments. Fireworks AI offers a dynamic setting where collaboration with top-tier engineers and researchers drives innovation in generative AI.
Joblaze insights
Quick facts
- How much experience is required?
- At least 3 years of relevant experience for this Member of Technical Staff, AI Training Infrastructure role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, Docker, GCP, Kubernetes, PyTorch.
- What seniority level is this role?
- Fireworks AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff, AI Training Infrastructure role at Fireworks AI.
From the original posting
The Role:
As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.
Key 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
Minimum Qualifications:
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, orchestration (Kubernetes, Docker)
Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)
Preferred Qualifications:
Master's or PhD in Computer Science or related field
Experience training large language models or multimodal AI systems
Experience with ML workflow orchestration tools
Background in optimizing high-performance distributed computing systems
Familiarity with ML DevOps practices
Contributions to open-source ML infrastructure or related projects
Why Fireworks?
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Standard company text repeated across Fireworks AI's postings is omitted here.