About the job
Do you want to join an innovative team of scientists who develop Agentic AI, LLM, and deep learning based solutions to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support, risk mitigation and provide the best customer and seller experience? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience? Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and create solutions that have cross-organizational impacts? If yes, then you may be a great fit to join the Machine Learning Accelerator team.
Responsibilities
Research and prototype AI and Machine Learning applications that solve strategic business problems across Selling Partner Experience (SPX) domains; Collaborate with engineers and business partners to design and implement solutions at scale that are of broad benefit to SPX organizations; Develop large-scale solutions for high impact projects; Introduce tools and other techniques that can be used to solve problems from various perspectives; Influence the team’s technical strategy by making insightful contributions to the team’s priorities, approach and planning; Develop and introduce tools and practices that streamline the work of the team; Mentor junior team members and participate in hiring
Qualifications
Minimum
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience; 3+ years of building machine learning models or developing algorithms for business application 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; Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Preferred
Experience using Unix/Linux; Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning; Experience with LLM fine-tuning, in-context learning, or model evaluation; Hands-on experience designing reward models or RL post-training pipelines (PPO/GRPO, DPO) for LLMs or agents, including preference-data collection and evaluation; Experience building agentic AI systems — tool use, planning, retrieval-augmented generation, or multi-agent workflows; Experience with researching and developing neuro-symbolic solutions and applications; Publications at top ML/AI venues; Experience partnering with product/engineering teams to deliver ML in large-scale production system