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Senior AI Engineering Manager

221k – 331kNew York, NYEngineering ManagementOnsite7+ YOE
Summary

Lead a multidisciplinary team of AI research engineers building core language learning systems at Duolingo. Drive ML model development, team growth, and strategic decisions for personalization and experimentation.

About the role

Responsibilities

  • Manage a full-stack team of frontend, backend, and AI Research engineers, fostering a collaborative and innovative work environment.
  • Contribute to the development and training of a variety of machine learning models, including large-scale neural networks.
  • Collaborate with cross-functional teams to understand their needs and align the models’ outputs with company objectives.
  • Participate in and help drive strategic product and business decision-making as a member of the Language Learning leadership group.
  • Attract, grow, and retain diverse engineering talent; provide clear expectations, coaching, and career development, and build a healthy, inclusive team culture.
  • Stay up-to-date with the latest developments in machine learning and apply this knowledge to drive advancements in our projects.
  • Mentor team members, providing guidance and support in their professional development.
  • Ensure the delivery of high-quality, scalable, and efficient machine learning solutions.

Requirements

  • Experience leading, managing, and building a team of software engineers, with demonstrated ability to hire, develop, and retain talent.
  • Proven experience as an AI research engineer, for example, in LLMs, bandits, computer vision, or related fields.
  • Strong background in training and fine-tuning large models in an applied setting.
  • 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 across engineering, product, design, and data; clear written specs and status updates.
  • Technical depth sufficient to guide architecture and implementation trade-offs, evolve quality standards, and mentor engineers on best practices.
  • Deep understanding of machine learning concepts, frameworks, and best practices.
Skills
Machine LearningLarge Language ModelsReinforcement LearningFine-tuningFeature EngineeringRecommender SystemsExperimentationPythonNeural NetworksModel Deployment
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