Applied Scientist, AGI Customization Services

Key job responsibilities

• Contribute to the development of novel customization techniques including extended post-training, continued pre-training, and advanced knowledge distillation
• Collaborate with cross-functional teams to design and implement enterprise-ready tooling for various training techniques on Amazon SageMaker
• Design and execute experiments to optimize model accuracy, latency, and cost across different customization approaches (SFT, DPO, PPO)
• Create robust evaluation frameworks for assessing model performance across different domains and use cases
• Contribute to the development of the Responsible AI toolkit, including creating training and evaluation datasets for model alignment
• Design and implement secure access mechanisms for early model checkpoints and weights
• Communicate technical insights and results to both technical and non-technical stakeholders through presentations and documentation

Basic Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• 1+ years of building machine learning models for business application experience
• Master's degree, or PhD and 2+ years of applied research experience
• Experience with any programming language such as Python, Java, C++
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• PhD in computer science, machine learning, engineering, or related fields, or Master's degree
• PhD in computer science, computer engineering, or related field, or experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and PyTorch
• Experience that includes strong analytical skills, attention to detail, and effective communication abilities, or experience in software development and experience in managing and troubleshooting network
• Experience collaborating with cross-functional teams
• Experience in developing and implementing algorithms and models for supervised fine-tuning and reinforcement learning
• Experience with patents or publications at top-tier peer-reviewed conferences or journals