Seeking an Applied Algorithms Researcher for groundbreaking work in AI code generation at the forefront of GPT-4 technology. Ideal candidates are pioneers in AI with academic and applied research backgrounds, ready to innovate in code generation, evaluation flows, and multi-agent architectures.

About the Role

Codegen seeks an Applied Algorithms Researcher to push the boundaries of what GPT-4 and subsequent models can achieve, focusing on the core research machine and algorithms for code generation.

This includes evaluation flows, multi-agent architectures, and code modification methodologies. The ideal candidate possesses a blend of academic research experience in AI from the pre-LLM era, hands-on software engineering outside academia, and a specific research interest in code generation and multi-agent systems.

We value individuals who are prolific in developing research code to production quality and capable of self-guided research. This role is perfect for someone passionate about the rapid progress in AI, who prefers the dynamism of a cutting-edge research environment over traditional academia.

Our technology ecosystem is Python-centric, employing OpenAPI APIs, FastAPI, and Modal for backend development. We emphasize deep git integration, use Retool for internal applications, and maintain a minimal frontend using Remix.js and TypeScript.

Qualifications

• Academic research experience in AI, preferably before the large language model (LLM) era, demonstrating a deep curiosity about AI.

• Software engineering experience outside the university, ideally in applied research settings like Google, DeepMind, Anthropic, etc.

• Focused research interest in code generation and multi-agent systems.

• Proven ability to develop research code and elevate it to production quality.

• Self-guided research capabilities.

• A strong desire to contribute to fast-paced AI advancements outside of academia.

Responsibilities

The selected candidates will be tasked with:

• Expanding our AI capabilities, including setting up evaluation flows, developing core algorithms for knowledge management and updates, and establishing the core architecture for generating AI-driven solutions.

• Prototyping new solutions and collaborating with the engineering team to transition these into production.

• Engaging in significant exploratory data analysis and working closely with senior software engineers to implement sophisticated algorithms.