Engineering Manager, MLE
Lead ML engineering on OpenAI's Integrity team to build, deploy, and optimize LLMs and classifiers for content understanding, abuse prevention, and platform safety. Requires advanced degree, deep learning expertise, and LLM fine-tuning experience.
About the job
Responsibilities
- 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.
- Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions.
- Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready.
- Engage 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.
- Monitor and maintain deployed models to ensure they continue delivering value.
Requirements
- 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.
- Familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization.
- 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 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.
Nice-to-Haves
- Experience with content understanding or abuse prevention with LLMs.
Skills
PyTorch, TensorFlow, Deep Learning, Transformers, Llm Fine-Tuning, Distillation, Supervised Fine-Tuning, Policy Optimization, Data Structures, Algorithms, Machine Learning
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