Senior Research Engineer, LLM Training & Post-Training

Lightning AI builds an end-to-end platform for developing, training, and deploying AI systems. The Senior Research Engineer will advance the training, fine-tuning, evaluation, and deployment of large language models while improving model quality, training efficiency, and developer productivity. The role collaborates with researchers, infrastructure engineers, platform teams, and customers to turn research into production-ready AI systems.

Responsibilities:

• Design, build, and optimize training and post-training pipelines for large language models
• Improve model quality through supervised fine-tuning, continued pretraining, preference optimization, reinforcement learning, evaluation, and experimentation
• Build and improve PyTorch-based training infrastructure, tooling, and developer workflows
• Optimize distributed training across multi-GPU environments by improving throughput, memory efficiency, scalability, and GPU utilization
• Investigate model training issues, including convergence, instability, communication overhead, and performance bottlenecks
• Design evaluation methodologies, benchmark models, analyze failure modes, and acheive model improvements through experimentation
• Collaborate directly with customers to understand real-world workloads and translate those learnings into improvements across Lightning AI's research platform
• Partner closely with research, infrastructure, and platform engineering teams to build production-ready AI systems
• Contribute to open-source projects through new features, tooling improvements, documentation, and community engagement

Requirements:

• Significant experience training, fine-tuning, evaluating, and/or optimizing transformer-based language models using PyTorch
• Experience with modern LLM training and post-training techniques such as continued pretraining, SFT, RLHF, preference optimization (DPO, PPO, GRPO), reward modeling, or similar approaches
• Strong understanding of distributed training and multi-node systems, with experience improving training performance, scalability, and/or efficiency
• Strong software engineering fundamentals, including building production-quality Python software and research tooling
• Experience designing experiments, evaluating model performance, and debugging complex training and/or optimization issues
• Excellent communication and collaboration skills, including the ability to work effectively across research, product, infrastructure, and customer-facing engagements
• Comfortable working in fast-moving, ambiguous environments where priorities evolve over time
• Master's degree, PhD, or equivalent industry experience in Machine Learning, AI, Computer Science, or a related field
• DeepSpeed, FSDP, Megatron-LM, Hugging Face Transformers, TRL, PEFT, Lightning Fabric, NVIDIA Molt, or similar training frameworks
• CUDA, Triton, vLLM, SGLang, TensorRT, or other AI systems and performance optimization technologies
• GPU performance optimization, mixed precision, memory optimization, or distributed training optimization
• Open-source contributions, research publications, or production AI platforms supporting large-scale training or inference workloads
• Startup experience or experience working on highly cross-functional engineering teams