Staff AI Research Engineer

Duolingo Duolingo · Consumer · New York, NY +1 · AI + Machine Learning Engineering

Staff AI Research Engineer at Duolingo focused on the Monetization team, developing and deploying AI systems including bandit models and large-scale neural networks to improve the learning experience and drive business objectives. The role involves full-stack ML work from feature engineering to deployment and monitoring, with a strong emphasis on LLMs and bandits.

What you'd actually do

  1. Contribute to the development and training of bandit models and large-scale neural network models.
  2. Collaborate with cross-functional teams to understand their needs, to align the models’ outputs with company objectives.
  3. Participate in and help drive strategic product and business decision-making as a member of the Monetization leadership group.
  4. Stay up-to-date with the latest developments in machine learning, particularly in LLMs and bandits, and apply this knowledge to drive advancements in our projects.
  5. Ensure the delivery of high-quality, scalable, and efficient machine learning solutions.

Skills

Required

  • AI research engineering
  • large language models
  • multi-armed bandits
  • feature engineering
  • training data development
  • fine-tuning
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring
  • machine learning concepts
  • frameworks
  • best practices
  • leadership skills
  • communication skills

Nice to have

  • computer vision

What the JD emphasized

  • proven track record as an AI research engineer
  • extensive experience across various machine learning techniques including large language models, multi-armed bandits, and more
  • experience at all levels of the ML stack, including feature engineering, developing training data, fine-tuning, reinforcement learning, quality evaluations, deployment, and monitoring
  • bandit models
  • large-scale neural network models
  • LLMs
  • bandits
  • Strong background in training and fine-tuning large models in an applied setting

Other signals

  • developing bandit models
  • large-scale neural network models
  • fine-tuning
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring
  • LLMs
  • bandits