Global Optimization is a strategic initiative aimed at improving Amazon advertisers experience at global scale. We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows at global scale. 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 that improve advertisers experiences globally
- Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO).
- Design and implement optimization models that work at global scale taking into account nuances of multiple countries
- Innovate new science models to help advertisers scale their campaigns globally
- 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.
- Collaborate with peers across engineering and product to bring scientific innovations into production.
- Stay current with the latest research in LLMs, RL, and agent-based AI, optimization and translate findings into practical applications.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 3+ years of building models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience in designing experiments and statistical analysis of results
PREFERRED QUALIFICATIONS
- Experience in professional software development
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience with LLMs, AI Agents, MCPs, Chain of Thought reasoning
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, CA, Palo Alto - 171,600.00 - 222,200.00 USD annually
