Applied Scientist, Amazon Leo Satellite Build Systems

Amazon; Redmond, Northern (WA, KY); Hybrid; USD 170,000 - 260,000; Full time

Benefits offered by this job

Health insurance

RSUs (Restricted Stock Units)

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Leo Satellite Build Systems is seeking an Applied Scientist to translate manufacturing challenges into rigorous ML problems. You will design and deploy ML models, including LLM-based and retrieval systems, using large-scale datasets with imperfect ground truth.

You will collaborate with Manufacturing, Quality, and engineering partners, building production-ready components with robust testing and monitoring. A strong publication or patent record is valued.

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.

Responsibilities

• Translate manufacturing problems into defined scientific problems and evaluation criteria.
• Design, train, and deploy ML models including LLM-based systems and retrieval models.
• Develop models using large-scale, noisy datasets with imperfect ground truth.
• Apply techniques in anomaly detection, root-cause inference, multimodal learning, IR, and generative AI.
• Design experiments and evaluation frameworks addressing real-world failure modes and latency.
• Balance model complexity, data quality, accuracy, latency, cost, and maintainability.
• Build production-quality components with testing, documentation, and monitoring.
• Collaborate with Manufacturing, Quality, Test, and engineering to meet customer needs.
• Analyze performance, identify gaps, and iteratively improve deployed solutions.
• Document approaches, results, and decisions for reproducibility.

Skills

Java

C++

Python

Education

PhD, or Master’s degree in CS/CE/ML

Tools

Unix/Linux

Big Data Tools

Job description

Applied Scientist, Amazon Leo Satellite Build Systems

Job ID: 10498998 | Amazon Kuiper Manufacturing Enterprises LLC

Key job responsibilities

• Translate ambiguous manufacturing and operational problems into well-defined scientific problems, modeling approaches, and evaluation criteria
• Design, train, and deploy machine learning models, including LLM-based systems, retrieval models, and task-specific models
• Develop and evaluate models using large-scale, noisy, heterogeneous datasets with incomplete, delayed, or imperfect ground truth
• Apply state-of-the-art techniques in areas such as anomaly detection, root-cause inference, multimodal learning, information retrieval, and generative AI, adapting or extending them to meet project requirements
• Design experiments and evaluation frameworks that capture real-world failure modes, distribution shift, and decision risk
• Make principled tradeoffs among model complexity, data quality, accuracy, latency, cost, and maintainability
• Build production-quality scientific components with appropriate testing, documentation, monitoring, and operational mechanisms
• Work with Manufacturing, Quality, Test, and engineering partners to understand customer needs and translate them into effective scientific solutions
• Analyze model and system performance, identify gaps and root causes, and iteratively improve deployed solutions
• Clearly document scientific approaches, experimental results, design decisions, and lessons learned so that others can understand and reproduce the work
• Contribute to technical discussions, mentor less experienced teammates, and help advance scientific and engineering best practices within the team

A day in the life

You may start by partnering with Quality and Manufacturing teams to define a training dataset for a root-cause prediction model, including how historical cases should be labeled and evaluated. You then design experiments and train models, comparing approaches across architectures, features, and data slices. Later, you analyze benchmark results to identify failure modes, data-quality issues, and generalization gaps, and refine the evaluation set to better represent real-world cases. You work with engineers to integrate the model into a production workflow, adding testing, monitoring, and feedback mechanisms. Throughout the day, you balance scientific rigor with practical constraints such as data availability, latency, reliability, and operational cost.

About the team

Leo Satellite Build Systems is the centralized AI team within Leo Production Operations. We build shared capabilities for AI across Production Operations, including governed data assets, machine learning models, retrieval systems, evaluation frameworks, and knowledge services.

We work on real-world systems where scientific decisions can influence physical outcomes. We value rigorous experimentation, strong data foundations, clear documentation, and production-ready engineering. Our team is helping enable AI-native manufacturing by turning fragmented operational knowledge and data into reliable intelligence that improves production outcomes.

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

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• Experience with one or more areas such as natural language processing, information retrieval, multimodal learning, anomaly detection, causal or root-cause inference, or generative AI
• Experience training or deploying LLM-based systems, retrieval-augmented generation (RAG), or other modern AI systems
• Experience designing evaluation datasets and methodologies for production machine learning systems
• Experience working with noisy, incomplete, delayed, or weakly labeled data
• Experience adapting or extending state-of-the-art research techniques to solve practical business problems
• Experience building reliable, testable, and maintainable machine learning components for production environments
• Experience working with engineering, manufacturing, quality, test, or operations teams
• Experience in manufacturing, aerospace, robotics, or other complex physical-world systems
• Experience with governed, access-controlled, or compliance-constrained data environments
• Experience communicating scientific methods, results, and tradeoffs through clear technical documentation or research publications

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

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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 .

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