דרושים Sr Applied Scientist ב-אמזון ב-United States, California, Palo Alto

About Sponsored Products and BrandsKey job responsibilities
As a Software Development Engineer in Machine Learning, you will:
* Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems.
* Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.
* Work closely with applied scientists to optimize the performance of machine-learning models and infrastructure, and implement end-to-end solutions.

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent

Key job responsibilities
- Integrate with a diverse array of internally and externally developed hardware and software services
- Implement continuous integration and continuous deployment (CI/CD) pipelines
- Validate solutions via software-in-the-loop simulation- Develop infrastructure to support development, operations, and maintenance of low level software for warehouse automation systems

- Experience in automating, deploying, and supporting large-scale infrastructure
- Experience programming with at least one modern language such as Python, Ruby, Golang, Java, C++, C#, Rust
- Experience with Linux/Unix
- Experience with CI/CD pipelines build processes
- 2+ years of non-internship professional software development experience

- Experience with distributed systems at scale
- Bachelor's degree
- Experience utilizing AWS cloud solutions in a DevOps environment

You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns.Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production.Key job responsibilities- Design and build agents to guide advertisers in conversational and non-conversational experience.
- Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).
- Curate datasets and tools for MCP.
- Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails.
- Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning.
- Prototype and iterate on multi-agent orchestration frameworks and workflows.- Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications.

- PhD, or Master's degree and 6+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.We’re looking for Senior Data Scientists capable of using AI/ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.Key job responsibilities
As an experienced Senior Data Scientist, you will be responsible for:
1. Lead end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, solution deployment, and post-production monitoring
2. Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate AI algorithms and build ML systems and operations (MLOps) using AWS services to address real-world challenges4. Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations tailored to technical, business, and executive stakeholders
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Why AWS
Work/Life BalanceMentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- Master's degree in computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field with 5+ years of experience; or bachelor's degree with 8+ years of experience
- 5+ years of building machine learning models for business application experience
- 3+ years of hands-on experience with training, fine-tuning, evaluating, and deploying transformer models in production
- Experience with cloud services related to machine learning (e.g., Amazon SageMaker) and generative AI applications
- Experience with technical customer-facing engagements, and strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to non-experts

- PhD in computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, VPC) and professional certifications (e.g., Solutions Architect Professional)
- 2+ years of experience with design, deployment, and evaluation of AI agents and orchestration approaches; experience with open source frameworks like LangChain, LangGraph, LlamaIndex, and/ or similar tools
- 5+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience using PyTorch or TensorFlow
- Experience in launching AI applications in production on AWS
- Experience building ML pipelines with MLOps best practices, including: data preprocessing, distributed & GPU training, model deployment, monitoring, and retraining; experience with container and CI/CD pipelines

Key job responsibilities
As a Senior Software Development Engineer in Redshift, you will build systems that benefit Redshift clusters across the world. You will take significant ownership on the overall product vision by defining new features and architecture. You will distill complex and ambiguous requirements, converting them into meaningful projects that provide immense customer value. You will own and code end-to-end deliverables from design, development, deployment, monitoring, and operations. You will lead engineers on substantial projects and help develop a flourishing team through active mentoring and hiring.
A day in the life
As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also:Participate in design discussions, code review, and communicate with internal and external stakeholders.Work in a startup-like development environment, where you’re always working on the most important stuff.Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.About AWSWork/Life Balance
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- 4+ years of distributed systems experience, or Bachelor's degree in computer science, engineering, mathematics or equivalent
- Experience with distributed computing and enterprise-wide systems Proficiency in at least one modern programming language like Java, C++, Rust, or Python building and operating mission-critical, highly available systems

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Master's degree in computer science or equivalent
- Master's degree in Computer Science, Computer Engineering, Data Science, Information Systems, or related STEM fields, or experience in writing algorithms and creating data structures
- Experience with distributed systems at scale
- Experience performing live system and core analysis using GDB

Key job responsibilities
As a Senior Software Development Engineer in Redshift, you will build systems that benefit Redshift clusters across the world. You will take significant ownership on the overall product vision by defining new features and architecture. You will distill complex and ambiguous requirements, converting them into meaningful projects that provide immense customer value. You will own and code end-to-end deliverables from design, development, deployment, monitoring, and operations. You will lead engineers on substantial projects and help develop a flourishing team through active mentoring and hiring.
A day in the life
As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects.You’ll also:Participate in design discussions, code review, and communicate with internal and external stakeholders.Work in a startup-like development environment, where you’re always working on the most important stuff.Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.About AWSWork/Life Balance
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- Experience with distributed computing and enterprise-wide systems Proficiency in at least one modern programming language like C++, Java, or Rust building and operating mission-critical, highly available systems

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Master's degree in computer science or equivalent
- Requires strong coding skills with emphasis on logical thinking, data structures, algorithms, and problem-solving capabilities.
- Experience with distributed (multi-tiered) systems architecture
- Experience performing live system and core analysis using GDB

About Sponsored Products and Brands
Key job responsibilities
As a Sr Applied Scientist, you will:* Develop scalable and effective machine learning models and optimization strategies to solve business problems.
* Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business.
* Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems.
* Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience with neural deep learning methods and machine learning
- Experience programming in Java, C++, Python or related language

- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.