Senior / Lead Research Scientist
Inworld AI is a research lab and inference provider for realtime AI at consumer scale. It provides text-to-speech, speech-to-text, speech-to-speech, and LLM routing products for developers and businesses building consumer-facing applications.
Maintainer signals as of 9/2/2026
Funding history
Projects
About Inworld AI
Inworld AI develops and operates realtime AI infrastructure, including first-party text-to-speech and speech-to-text models, a customizable speech-to-speech Realtime API, and an LLM Router that routes requests across leading model providers. Its platform supports voice agents, social and companion applications, learning and education, health and wellness, agentic workforce applications, and games and media. The company serves developers, businesses, and teams building consumer-facing applications.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will take unclear research problems and make them clear. You will frame research questions, run experiments, evaluate model quality and failures, and turn results into shipped outcomes. You will work across foundation models, evaluation, and frontier AI topics, while communicating professionally in English with US-based leadership and engineering colleagues.
Requirements
- Demonstrate relevant experience in enough of the listed areas to make a case for the role.
- Have experience with foundation models, including training, architectures, reinforcement learning, reward modeling, or scaling.
- Have experience with evaluation, including benchmarks, evaluation loops, quality measurement, LLM-as-judge, or failure analysis.
- Have experience in frontier topics such as multimodal models, agents, tool use, test-time compute, or world models.
- Have published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI, or demonstrate equivalent relevant experience.
- Hold a PhD in machine learning or natural language processing, or have equivalent practical experience.
- Demonstrate non-trivial AI side projects, interdisciplinary experiments, or open-source contributions.
- Demonstrate full-stack research ownership.
- Have professional written and spoken English fluency.
Responsibilities
- Frame research questions and design experiments.
- Run experiments and analyze results.
- Develop and use evaluation loops, benchmarks, quality measurements, and failure analysis.
- Own research end to end, from framing questions through shipping results.
- Contribute to research on foundation models, multimodal models, agents, tool use, test-time compute, or world models.
- Collaborate daily in written and spoken English with US-based leadership and engineering teams.