亚马逊Senior Machine Learning Engineer, Generative AI Innovation Center
任职要求
基本任职资格
- 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
优先任职资格
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and opera…工作职责
* Large-Scale Training Pipelines: Design and implement distributed training pipelines for LLMs using tools such as Fully Sharded Data Parallel (FSDP) and DeepSpeed, ensuring scalability and efficiency * LLM Customization & Fine-Tuning: Adapt LLMs for new languages, domains, and vision applications through continued pre-training, fine-tuning, and Reinforcement Learning with Human Feedback (RLHF) * Model Optimization on AWS Silicon: Optimize AI models for deployment on AWS Inferentia and Trainium, leveraging the AWS Neuron SDK and developing custom kernels for enhanced performance * Customer Collaboration: Interact with enterprise customers and foundational model providers to understand their business and technical challenges, co-developing tailored generative AI solutions
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. 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 Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. As an Applied Scientist, you will - Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges - Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production - Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization - Provide customer and market feedback to product and engineering teams to help define product direction
• Develop next-generation AI experiences for Microsoft Edge — leverage machine learning and generative AI to reinvent how users browse, search, and interact with the web. • Advance Edge’s contextual intelligence by building models that synthesize browsing history, page content, and user intent to deliver proactive, personalized, and trustworthy assistance. • Drive innovation in agentic systems, prototyping and productionizing conversational, reasoning, and planning models that transform Edge from a static browser into a true AI companion. • Collaborate cross-functionally with product managers, designers, and engineers to translate AI capabilities into elegant, high-utility user experiences. • Own the full AI feature development lifecycle — from data pipeline and evaluation metric design to model and prompt tuning, quality validation, and continuous improvement.
• Develop next-generation AI experiences for Microsoft Edge — leverage machine learning and generative AI to reinvent how users browse, search, and interact with the web. • Advance Edge’s contextual intelligence by building models that synthesize browsing history, page content, and user intent to deliver proactive, personalized, and trustworthy assistance. • Drive innovation in agentic systems, prototyping and productionizing conversational, reasoning, and planning models that transform Edge from a static browser into a true AI companion. • Collaborate cross-functionally with product managers, designers, and engineers to translate AI capabilities into elegant, high-utility user experiences. • Own the full AI feature development lifecycle — from data pipeline and evaluation metric design to model and prompt tuning, quality validation, and continuous improvement.
Design, training/evaluation, development/deployment of advanced models (LLM/MLLM/SLM) for ads content creation, ads performance optimization, and automated customer support. Apply strong problem-solving and data analysis skills to identify opportunities, optimize performance, and deliver measurable business impact.Stay up to date with the latest advancements in generative AI, LLM-driven agent, online advertisement, and make integration and improvement for our systems.Develop scalable and robust algorithms and pipelines for efficient model training, data processing, online serving and measurement.Collaborate with cross-functional teams, such as product managers and Infra/UI engineers, to define strategies and deliver solutions that enhance our systems.Present problems, insights, technical solutions, and results clearly to both technical and non-technical stakeholders.