Senior AI Engineering Manager

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

Senior AI Engineering Manager at Duolingo to lead a multidisciplinary team of AI research engineers. The role involves managing the full ML stack, from feature engineering and data training to fine-tuning, reinforcement learning, deployment, and monitoring. The focus is on building AI systems that enhance the core learning features of Duolingo, improving user experience and habit formation. Requires experience in LLMs, personalization, recommender systems, and experimentation.

What you'd actually do

  1. Manage a full-stack team of frontend, backend, and other AI Research engineers, fostering a collaborative and innovative work environment.
  2. Contribute to the development and training of a variety of machine learning models, including large-scale neural networks.
  3. Collaborate with cross-functional teams to understand their needs, to align the models’ outputs with company objectives.
  4. Participate in and help drive strategic product and business decision-making as a member of the Language Learning leadership group.
  5. Attract, grow, and retain diverse engineering talent; provide clear expectations, coaching, and career development, and build a healthy, inclusive team culture.

Skills

Required

  • Experience leading, managing, and building a team of software engineers
  • Proven experience as an AI research engineer
  • Strong background in training and fine-tuning large models
  • Advanced degree in Computer Science, Engineering, or a related field with a focus on machine learning or artificial intelligence, or equivalent experience
  • Excellent communication and stakeholder management skills
  • Technical depth sufficient to guide architecture and implementation trade‑offs
  • Deep understanding of machine learning concepts, frameworks, and best practices

Nice to have

  • LLMs
  • bandits
  • computer vision
  • personalization
  • recommender systems
  • experimentation
  • feature engineering
  • developing training data
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring

What the JD emphasized

  • proven track record leading multidisciplinary teams
  • extensive experience across various machine learning techniques
  • comfortable working at all levels of the ML stack
  • building the AI systems that support the core learning features
  • improving the learning experience
  • helping our learners build healthy, productive habits
  • Experience leading, managing, and building a team of software engineers
  • 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

  • managing AI research engineers
  • large language models
  • personalization
  • recommender systems
  • experimentation
  • ML stack
  • feature engineering
  • training data
  • fine-tuning
  • reinforcement learning
  • quality evaluations
  • deployment
  • monitoring
  • AI systems that support core learning features
  • improving learning experience
  • building healthy, productive habits