ML Research Engineer, ML Systems
Builds and optimizes distributed frameworks for LLM training and inference on Scale's RLXF platform. Collaborates with ML teams to accelerate research, requiring expertise in PyTorch, CUDA, transformers, and large-scale distributed systems.
About the job
You will:
- Build, profile and optimize our training and inference framework
- Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
- Research and integrate state-of-the-art technologies to optimize our ML system
Ideally you’d have:
- Strong excitement about system optimization
- Experience with multi-node LLM training and inference
- Experience with developing large-scale distributed ML systems
- Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.
- Strong written and verbal communication skills and the ability to operate in a cross functional team environment
Nice to haves:
- Demonstrated expertise in post-training methods or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.
Skills
PyTorch, CUDA, Transformers, Flash Attention, Llm Training, Distributed Ml Systems, RLHF, Multi-Node Training, Ml Inference, System Optimization
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