Machine Learning Systems Engineer, RelationalAI

• Experience Level: 3+ years of experience in machine learning engineering or research
• Programming Languages: Proficiency in both C/C++ and Python
• High Performance Computing: Deep understanding of HPC concepts, including:
• MPI (Message Passing Interface) programming and optimization
• Bulk Synchronous Parallel (BSP) computing models
• Multi-GPU and multi-node distributed computing
• CUDA/ROCm programming experience preferred
• Solid understanding of gradient descent and backpropagation algorithms
• Experience with transformer architectures and the ability to explain their mechanics
• Knowledge of deep learning training and its applications
• Understanding of distributed training techniques (data parallelism, model parallelism, pipeline parallelism, large batch training, optimization)
• Publications: Experience with machine learning research and publications preferred
• Research Skills: Ability to read, understand, and implement techniques from recent ML research papers
• Open Source: Demonstrated commitment to open source development and community collaboration
• 3+ years of experience in machine learning engineering or research
• Experience with large-scale distributed training frameworks (Megatron-LM, DeepSpeed, FairScale, etc.)
• Familiarity with inference optimization frameworks (vLLM, TensorRT, etc.)
• Experience with containerization (Docker, Kubernetes) and cluster management
• Background in systems programming and performance optimization
• PhD or MS in Computer Science, Computer Engineering, Machine Learning, or related field

Desirable

• This role will involve heavily working with the ScalarLM framework and team
• As a Machine Learning Engineer, you will contribute directly to our machine learning infrastructure, to the ScalarLM open source codebase, and build large-scale language model applications on top of it
• You’ll operate at the intersection of high-performance computing, distributed systems, and cutting-edge machine learning research, developing the fundamental infrastructure that enables researchers and organizations worldwide to train and deploy large language models at scale
• This is an opportunity to take on technically demanding projects, contribute to foundational systems, and help shape the next generation of intelligent computing
• This role will contribute code and performance improvements to the open source project
• Develop and optimize distributed training algorithms for large language models
• Implement high-performance inference engines and optimization techniques
• Work on integration between vLLM, Megatron-LM, and HuggingFace ecosystems
• Build tools for seamless model training, fine-tuning, and deployment
• Optimize performance of advanced GPU architectures
• Collaborate with the open source community on feature development and bug fixes
• Research and implement new techniques for self-improving AI agents