- Develop and execute on LLM platform strategy for Copilot that extend language model's capabilities.
- Prototype approaches by steering language models to drive response quality across a wide range of scenarios.
- Identify and prioritize platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution.
- Define and build measurable evaluations with relevant datasets to demonstrate quality improvements.
- Define, deploy and manage experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users.
- Partner with product teams to scale tool building and work with inference, agents and orchestration teams to resolve dependencies.
- Accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery.
- Bachelor's Degree AND 5 years experience in product management OR equivalent experience.
- 3 years of experience leading ambiguous product areas, defining requirements, developing roadmaps, and working with multi-disciplinary teams to execute them.
- 2 years of experience building ML-powered or LLM-powered products.
- Hands-on experience with LLM APIs (e.g. OpenAI, Anthropic, Azure OpenAI), embeddings, vector databases, and tool use.
- Hands-on experience with prompt design, context window management, and model evaluation.
- 2 years of hands-on experience in building retrieval and/or recommendation systems, driving measurable improvements in relevance, engagement, and conversion through data-driven experimentation. Skilled in translating user intent into product strategy and collaborating closely with engineering and ML teams to deliver scalable, personalized experiences.
- 1 years of experience in building LLM applications with familiarity in agent and orchestration frameworks, tool use, LLM evaluations and driving efficiency improvements.
- Technical depth in software development, data science and machine learning. While you are not expected to write code on the critical path, you’re able to define and execute on zero-to-one prototypes, reason over large amounts of data, and speak the language of the engineers and researchers you work with.
- Experience using and applying Large Language Models. LLMs will be your clay – you should know how to prompt engineer them, how to evaluate them, and understand how they are tuned.
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