We are seeking a highly skilled and hands-on Machine Learning Engineer specializing in large model post-training and alignment. This role focuses on designing, executing, and optimizing post-training pipelines to improve model performance, controllability, domain adaptation, and reasoning capabilities
You will work across the full lifecycle of post-training—from data strategy and reward modeling to reinforcement learning–based optimization and production-grade inference deployment
Lead and execute the full post-training pipeline for large language models (LLMs), including supervised fine-tuning, preference optimization, and reinforcement learning–based methods
Design and implement advanced training paradigms such as DPO (Direct Preference Optimization) and GRPO (Generalized Reward Policy Optimization)
Develop domain-specific data recipes, curation strategies, and augmentation pipelines to optimize task performance
Conduct post-training of specialized small models from scratch, including architecture selection, dataset construction, and optimization strategy
Build and refine Reward Models to support alignment and downstream optimization
Design and implement RLAIF (Reinforcement Learning from AI Feedback) closed-loop systems
Optimize inference efficiency and deploy models using low-latency serving frameworks such as vLLM and SGLang
Evaluate model performance using both automated benchmarks and human/AI feedback loops
Collaborate with research and infrastructure teams to productionize training and deployment workflows