# Senior AI Research Engineer

**Company:** [Duolingo](https://hotfix.jobs/companies/duolingo)
**Location:** New York, NY, Pittsburgh, PA
**Role:** AI Research
**Salary:** $197k – $267k/yr
**Experience:** 5+ years
**Skills:** LLMs, speech recognition, text-to-speech, multimodal modeling, Fine-Tuning, Reinforcement Learning, machine learning frameworks, feature engineering, model deployment, model monitoring, benchmarking, personalization, neural networks
**Posted:** 2026-07-29

> Senior AI Research Engineer building and training ML models (LLMs, speech recognition, TTS) to enhance Duolingo's language learning via video calls. Requires advanced ML experience, leadership skills, and an advanced degree or equivalent.

## Job Description

## Responsibilities
- Join a full-stack team of frontend, backend and other 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, speech recognition, and text-to-speech systems.
- Collaborate with cross-functional teams to understand their needs, to align the models’ outputs with company objectives.
- Participate in and influence strategic product and business decision making with members of the Language Learning leadership group.
- 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
- Proven experience as an AI research engineer, for example, in LLMs, multimodal modeling, speech, 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 leadership and communication skills, with the ability to lead and inspire a team.
- 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.

## Nice-to-Haves
- 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.
- Broad experience in AI/ML.

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