Applied Scientist, AGI Customization Services
Amazon · Cambridge, MA · Applied Science
mid
machine learning
Applied Scientist
ic
3+ yrs Bachelor's
· Posted Apr 15, 2026
$142,800 – $193,200
USD per year
Skills
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in Cambridge, MA. The posting calls out experience with Python, Java, TensorFlow, PyTorch and roughly 3+ years of relevant work. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $142,800–$193,200 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Cambridge, MA
- Experience
- 3+ years
- Education
- Bachelor's degree
- Department
- Applied Science
- Posted
- Apr 15, 2026
AI Summary
Applied Scientist developing customization techniques for Amazon Nova language models, including fine-tuning, reinforcement learning, and knowledge distillation. Designs enterprise tooling on SageMaker, optimizes model performance, and creates evaluation frameworks. Requires 3+ years experience in machine learning or related field.
Job description
from Amazon careersThe 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.
As an Applied Scientist, you will play a important role in developing advanced customization capabilities that enable enterprises to build highly performant application-specific models without the need for training models from scratch. Your work will directly impact how companies leverage Amazon Nova models for their specific use cases.
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)
- 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
- 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
- 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 troublshooting 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
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
USA, MA, Cambridge - 142,800.00 - 193,200.00 USD annually
USA, WA, BELLEVUE - 142,800.00 - 193,200.00 USD annually
As an Applied Scientist, you will play a important role in developing advanced customization capabilities that enable enterprises to build highly performant application-specific models without the need for training models from scratch. Your work will directly impact how companies leverage Amazon Nova models for their specific use cases.
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)
- 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 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 troublshooting 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
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, MA, Cambridge - 142,800.00 - 193,200.00 USD annually
USA, WA, BELLEVUE - 142,800.00 - 193,200.00 USD annually
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