Amazon Sr Applied Scientist SPB Advertiser Guidance 

United States, California, Palo Alto 

792922241

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.