Senior Computer Scientist

What you'll do

• Define the architecture for agent learning and feedback systems to evaluate outcomes and capture quality signals.
• Design pipelines to translate quality signals into prompt updates, routing changes, and reward data.
• Apply machine learning techniques including preference learning, reward modeling, and reinforcement learning to production systems.
• Integrate the Learning Layer with the Agent Harness, evaluation infrastructure, skills layer, and context management.
• Establish architecture standards through design documents, technology evaluations, and build-versus-buy decisions.
• Develop prototypes and proof-of-concepts using current AI models and agent frameworks to test production scalability.
• Mentor engineers through design reviews, code reviews, and the creation of technical documentation.
• Translate business goals into a clear technical roadmap and executable architecture for platform stakeholders.

What we're looking for

• 12+ years of software engineering experience, including designing and architecting AI, ML, or data-intensive systems at scale.
• Experience crafting and delivering LLM and agentic systems, including tool use, context management, and output evaluation.
• Experience with reinforcement learning, preference learning, RLHF, or reward modeling applied to production systems.
• Experience designing evaluation and learning systems that connect agent actions to measurable outcomes and feedback signals.
• Demonstrated ability to translate ambiguous business or technical objectives into clear architecture and execution plans.
• Experience applying AI or machine learning to products or platforms with measurable results in quality, reliability, or cost efficiency.
• Experience providing technical leadership through architecture reviews, technical standards, and mentoring engineering teams.
• Proficiency in Python and at least one additional programming language.
• Experience with cloud platforms such as AWS or Azure, data pipeline technologies, and ML experimentation infrastructure.