Applied Scientist, AGI Customization Services

Amazon; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading tech company is seeking an experienced Applied Scientist to join its AGI Customization Team in San Francisco. This role focuses on developing innovative services and tools for model customization using techniques such as supervised fine-tuning and reinforcement learning. The ideal candidate will have extensive experience in machine learning, proficiency in programming languages, and a strong background in algorithms and data structures. This position offers the opportunity to work on cutting-edge AI technology within a collaborative environment.

Qualifications

• 3+ years of building models for business applications.
• PhD or Master’s degree with 4+ years of CS/CE/ML or related experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience with state-of-the-art deep learning model architecture design and training.

Responsibilities

• Develop novel customization techniques for model optimization.
• Collaborate with teams to implement tooling on Amazon SageMaker.
• Design experiments to optimize model accuracy and costs.
• Develop evaluation frameworks for model performance assessment.

Skills

Building machine learning models

Java

C++

Python

Algorithms and data structures

Numerical optimization

Data mining

Parallel computing

Deep learning

Education

PhD or Master’s degree in CS/CE/ML or related experience

Tools

MXNet

TensorFlow

Caffe

PyTorch

Job description

Applied Scientist, AGI Customization Services

The Artificial General Intelligence (AGI) Customization Team is seeking a highly skilled and experienced Applied Scientist to support adoption and enable customization of Amazon Nova. The role focuses on developing state‑of‑the‑art services and tools for model customization, including supervised fine‑tuning, reinforcement learning, and knowledge distillation across large language models.

Key Responsibilities

• Develop novel customization techniques such as 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).
• Develop and enhance preference learning algorithms and training curricula for customer‑specific applications.
• 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 applications.
• PhD or Master’s degree with 4+ years of CS/CE/ML or related experience.
• Experience in patents or publications at top‑tier peer‑reviewed conferences or journals.
• Proficiency in Java, C++, Python, or related languages.
• Background in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, or high‑performance computing.
• 1+ year of building machine learning models for business applications.
• 2+ years of applied research experience.
• Experience with state‑of‑the‑art deep learning model architecture design and training, optimization, and model pruning.

Preferred Qualifications

• Experience using Unix/Linux.
• Professional software development experience.
• PhD or Master’s degree in computer science, machine learning, engineering, or related fields.
• Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe, and PyTorch.
• Strong analytical skills, attention to detail, and effective communication abilities.
• 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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.