Job description

What you’ll do - Design and implement highly efficient distributed training systems for large-scale deep learning models. - Optimize parallelism strategies to improve performance and scalability across hundreds or thousands of GPUs. - Develop low-level systems components and algorithms to maximize throughput and minimize memory and compute bottlenecks. - Productionize the training systems onto CentML Platform. - Collaborate with researchers and engineers to productionize cutting-edge model architectures and training techniques. - Contribute to the design of APIs, abstractions and UX that make it easier to scale models while maintaining usability and flexibility. - Profile, debug, and tune performance at the system, model, and hardware levels. - Participate in design discussions, code reviews, and technical planning to ensure the product aligns with business goals. - Stay up to date with the latest advancements in large-scale model training and help translate research into practical, robust systems. What you’ll need to be successful - Bachelor’s, Master’s, or PhD’s degree in Computer Science/Engineering, Software Engineering, related field or equivalent working experience. - 3+ years of experience in software development, preferably with Python and C++. - Deep understanding of machine learning pipelines and workflows, distributed systems, parallel computing, and high-performance computing principles. - Hands-on experience with large-scale training of deep learning models using frameworks like PyTorch, Megatron Core, DeepSpeed. - Experience optimizing compute, memory, and communication performance in large model training workflows. - Familiarity with GPU programming, CUDA, NCCL, and performance profiling tools. - Solid grasp of deep learning fundamentals, especially as they relate to transformer-based architectures and training dynamics. - Experience working with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes). - Ability to work closely with both research and engineering teams, translating evolving needs into robust infrastructure. - Excellent problem-solving skills, with the ability to debug complex systems. - A passion for building high-impact tools that push the boundaries of what’s possible with large-scale AI. Bonus points if you have - Experience building tools or platforms for ML model training or fine-tuning. - Experience building backends (e.g., using FastAPI) and frontend (e.g., using React). - Experience building and optimizing LLM inference engines (e.g., vLLM, SGLang). - Exposure to DevOps practices, CI/CD pipelines, and infrastructure as code. - Familiarity with MLOps concepts, including model versioning and serving.

Requirements