Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement.
Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making.
Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness.
Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions.
Implement reinforcement learning loops and self-improvement mechanisms for agent training.
Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization.
Maintain model reproducibility and version control, integrating with experiment tracking systems.
Contribute to cross-functional research efforts to improve learning strategies, fine-tuning methods, and generalization performance.
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