Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience
• 4+ years of experience in software engineering with a focus on ML infrastructure or distributed systems
• Hands-on expertise in distributed training frameworks (e.g., FSDP, DDP, ZeRO) and ML frameworks (e.g., PyTorch, Transformers)
• Strong understanding of GPU/accelerator performance optimization techniques
• Experience designing and operating large-scale systems in production, preferably cloud-native
• Excellent problem-solving and communication skills with cross-domain collaboration experience

Responsibilities

• Design, build, and maintain distributed training infrastructure for large-scale foundation models
• Implement scalable pipelines for fine-tuning across GPU/accelerator clusters
• Optimize training performance using techniques like FSDP, DDP, and mixed precision training
• Contribute to developer-friendly frameworks and tooling for efficient training workflows
• Collaborate with cross-functional teams to ensure systems meet real-world requirements
• Research and implement emerging techniques in training efficiency and model adaptation
• Participate in code reviews and system design discussions to uphold high engineering standards

Benefits

• Competitive compensation package
• Opportunity to be part of a rapidly growing startup in a cutting-edge engineering field
• Inclusive and supportive work culture fostering learning and growth
• Exposure to various ML startups, providing unique learning and networking opportunities