Senior Principal Machine Learning Engineer - LLM Pst - Training and Optimization

• Lead the fine-tuning and post-training optimization of large language models (LLMs) for diverse applications.

• Develop and implement techniques for model compression, quantization, pruning, and knowledge distillation to optimize performance and reduce computational costs.

• Conduct research on advanced techniques in transfer learning, reinforcement learning, and prompt engineering for LLMs.

• Design and execute rigorous benchmarking and evaluation frameworks to assess model performance across multiple dimensions.

• Collaborate with infrastructure teams to optimize LLM deployment pipelines, ensuring scalability and efficiency in production environments.

• Stay at the forefront of advancements in LLM technologies, sharing insights, driving innovation within the team, and leading agile development.

• Mentoring other team members, facilitating within/across team workshops, fostering a culture of technical excellence and continuous learning.

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

• 8+ years of experience in machine learning, with a focus on large-scale model development and optimization.

• Deep expertise in LLM and transformer architectures (e.g., GPT, BERT, T5).

• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

• Experience with distributed training techniques and large-scale data processing pipelines.

• Proven track record of deploying machine learning models in production environments.

• Familiarity with model optimization techniques, including quantization, pruning, and knowledge distillation.

• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.

• Excellent communication skills and ability to translate technical concepts for diverse audiences.

Preferred Qualifications:

• Experience with multi-modal LLMs or domain-specific fine-tuning.

• Knowledge of cloud-based ML platforms (e.g., AWS, GCP, Azure).

• Contributions to open-source ML projects or publications in top-tier conferences.

• Familiarity with MLOps practices and tools.