Senior AI Engineering Manager
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.
Senior AI Engineering Manager
Lead and mentor a multidisciplinary AI research engineering team building core language learning ML systems at Duolingo. Requires proven AI/ML leadership, large model training experience, and an advanced degree or equivalent.
221k – 331kPittsburgh, PAEngineering ManagementOnsite8+ YOE
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 model 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 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 (e.g., 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 LearningLLMsReinforcement LearningFine-TuningFeature EngineeringRecommender SystemsExperimentationPythonNeural NetworksModel Deployment
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