Translate broad architectural goals into concrete, surface-agnostic systems across AIM, Gemini, and Geo, and drive the technical implementation of the MCP2Agency bridge and ensure strict adherence to data governance policies (e.g., SIAN) in the codebase.
Lead the technical delivery of high-priority partner integrations within a globally distributed team.
Serve as a technical role model by multiplying team impact through code reviews, leading design doc reviews, and mentoring junior/mid-level engineers.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications
Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.
Proven experience building developer-facing platforms, SDKs, or third-party integration frameworks.
Familiarity with LLM orchestration, tool-use (function calling), and agentic workflows. Direct experience with Model Context Protocol (MCP).
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
