Research Engineer, Applied AI Engineering: OpenAI

OpenAI is hiring a Research Engineer for its Applied Group. This group is responsible for transforming groundbreaking research into real-world applications. As a Research Engineer, you will work with some of the brightest minds in AI, helping to deploy state-of-the-art models in production environments and turning research breakthroughs into tangible solutions with a direct impact on industries and society.

Responsibilities
Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems, bringing OpenAI's research from concept to implementation.

Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions.

Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready.

Learn and Lead: Stay current with the latest developments in machine learning and AI, participate in code reviews, and provide technical leadership.

Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value.

Requirements
Education: Master's or PhD degree in Computer Science, Machine Learning, Data Science, or a related field.

Experience:

Demonstrated experience in deep learning and transformers models.

Proficiency in frameworks like PyTorch or TensorFlow.

Strong foundation in data structures, algorithms, and software engineering principles.

Skills & Mindset:

Excellent problem-solving and analytical skills, with a proactive approach to challenges.

Ability to work collaboratively with cross-functional teams.

Ability to move fast in an environment where priorities may be loosely defined and have competing deadlines.

Enjoys owning problems end-to-end and is willing to acquire new knowledge to get the job done.

Bonus Experience:

Experience with search relevance, ads ranking, or LLMs.

Familiarity with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization.