Research Engineer / Machine Learning Engineer - Applied Voice

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

Research Engineer role focused on designing, building, and optimizing state-of-the-art speech models (speech-to-speech, transcribing, text to speech) and transforming research breakthroughs into tangible OpenAI speech products. Involves collaboration with cross-functional teams, implementing scalable data pipelines, and ensuring models are production-ready.

What you'd actually do

  1. Design and build advanced machine learning models that solve real-world problems.
  2. Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready.
  3. Work closely with software engineers, product managers and forward deployed engineers to understand complex business challenges, address customer concerns and deliver AI-powered solutions.
  4. Stay ahead of the curve by engaging with the latest developments in machine learning and AI.
  5. Monitor and maintain deployed models to ensure they continue delivering value.

Skills

Required

  • Master's/ PhD degree in Computer Science, Machine Learning, or a related field.
  • 2+ years of professional engineering experience in relevant roles at tech and product-driven companies.
  • 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

Nice to have

  • Experience with speech models is a plus.
  • 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.

What the JD emphasized

  • speech models
  • speech products
  • speech-to-speech
  • transcribing
  • text to speech

Other signals

  • design and build advanced machine learning models
  • implement scalable data pipelines
  • optimize models for performance and accuracy
  • production-ready models