Senior/ Machine Learning Engineer (LLM)

About PatSnap
Patsnap empowers IP and R&D teams by providing better answers, so they can make
faster decisions with more confidence. Founded in 2007, Patsnap is the global leader
in AI-powered IP and R&D intelligence. Our domain-specific LLM, trained on our
extensive proprietary innovation data, coupled with Hiro, our AI assistant, delivers
actionable insights that increase productivity for IP tasks by 75% and reduce R&D
wastage by 25%. IP and R&D teams collaborate better with a user-friendly platform
across the entire innovation lifecycle. Over 15,000 companies trust Patsnap to
innovate faster with AI, including NASA, Tesla, PayPal, Sanofi, Dow Chemical, and
Wilson Sonsini.

About US

We are seeking machine learning engineers specializing in Large Language Models
(LLMs) to join our AI team. The ideal candidate will drive innovation in LLM
experiments, training, and deployment, optimizing LLM capability end-to-end for
cutting-edge applications in our product.

Requirements

• Engage in the development and optimization of large-scale pre-training language
  models, including model architecture design, parallel training strategies, and
  performance improvements.
• Drive research and implementation of advanced LLM post-training techniques,
  including chain-of-thought tuning, preference alignment, and RL for reasoning.
• Develop and optimize data collection pipelines for model training, including data
  de-duplication, cleaning, and verification.
• Design and implement solutions for model deployment, including inference
  optimization and scaling strategies.
• Collaborate with cross-functional teams to apply LLM capabilities in various
  business scenarios, such as materials science.
• Stay current with the latest developments in the field and contribute to the
  company's technical roadmap.

Qualifications

• Master's or Ph.D. in Computer Science, AI, or related field.
• 5+ years of experience in machine learning, with specific focus on NLP and LLMs.
• Strong understanding of transformer architectures and modern LLM frameworks
  (BERT, GPT, T5).
• Extensive experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
• Strong programming skills in Python and proficiency with ML tools (Hugging Face,
  DeepSpeed).
• Proven track record in training and optimizing large-scale language models (10B+
  parameters) is preferred.
• Experience with distributed training systems (Megatron) and optimization
  techniques is preferred.

Desired Qualifications

• Publications in top-tier conferences (NeurIPS, ICML, ACL, EMNLP).
• Contributions to open-source ML projects.
• Experience with RL-based alignment techniques.
• Good problem-solving and analytical abilities.