Research Engineer, Applied AI Engineering

OpenAI OpenAI · AI Frontier · San Francisco, CA · Applied AI

Research Engineer role focused on deploying and optimizing advanced ML models into production, transforming research into real-world applications. Involves collaboration, scaling data pipelines, and ensuring model performance and maintenance.

What you'd actually do

  1. Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact.
  2. Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives.
  3. Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches.
  4. Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.
  5. Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large.

Skills

Required

  • Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field
  • 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
  • methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization
  • Excellent problem-solving and analytical skills
  • Ability to work collaboratively with cross-functional teams

Nice to have

  • search relevance, ads ranking or LLMs is a plus

What the JD emphasized

  • move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
  • Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done

Other signals

  • Deploying state-of-the-art models in production environments
  • Design and deploy advanced machine learning models
  • Implement scalable data pipelines, optimize models for performance and accuracy
  • Monitor and maintain deployed models