Senior AI Research Engineer

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

Senior AI Research Engineer at Duolingo focusing on building AI systems for the Video Call team to improve the learning experience. The role involves developing and training machine learning models, including LLMs and speech models, and contributing to strategic product decisions. Requires experience in training/fine-tuning large models, ML concepts, and leadership skills.

What you'd actually do

  1. Contribute to the development and training of a variety of machine learning models, including large-scale neural networks, speech recognition, and text-to-speech systems.
  2. Collaborate with cross-functional teams to understand their needs, to align the models’ outputs with company objectives.
  3. Participate in and influence strategic product and business decision making with members of the Language Learning leadership group.
  4. Stay up-to-date with the latest developments in machine learning 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

  • LLMs
  • multimodal modeling
  • speech
  • personalization
  • training large models
  • fine-tuning large models
  • machine learning concepts
  • frameworks
  • best practices
  • leadership skills
  • communication skills

Nice to have

  • benchmarking
  • feature engineering
  • developing training data
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring

What the JD emphasized

  • proven track record as an AI research engineer
  • experience across various machine learning techniques including large language models, speech models, benchmarking, and/or personalization
  • experience at multiple levels of the ML stack, including feature engineering, developing training data, fine-tuning, reinforcement learning, quality evaluations, deployment, and monitoring
  • proven experience as an AI research engineer
  • Strong background in training and fine-tuning large models in an applied setting
  • Technical depth sufficient to guide architecture and implementation trade‑offs, evolve quality standards, and mentor engineers on best practices.

Other signals

  • large-scale neural networks
  • speech recognition
  • text-to-speech systems
  • fine-tuning
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring
  • LLMs
  • multimodal modeling
  • speech
  • personalization