Senior / Lead Research Scientist
Inworld AI develops AI products for growing applications, helping developers go from prototype to production faster. Their offerings include advanced text-to-speech (TTS) technology and an upcoming Runtime product, aimed at enhancing consumer applications with expressive, real-time voice AI.
Maintainer signals as of 8/23/2026
Funding history
About Inworld AI
Inworld develops AI products for consumer applications. They offer a text-to-speech model that aims for high quality with better pricing, lower latency, more control, local serving options, and open training code. They also have a product called Inworld Runtime, which is currently in private preview. Their services are used by partners like XBOX, Ubisoft, NVIDIA, and Meta. They focus on helping developers go from prototype to production faster and increase experimentation velocity to deploy new AI improvements daily.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Join Inworld's research lab to work on ambiguous problems, design experiments, treat evaluation as a first-class research product, and own research end-to-end from framing questions through experimentation and shipping results.
Requirements
- Experience with foundation models, including training, new architectures, reinforcement learning, reward modeling, and scaling
- Experience with evaluation, including benchmarks, evaluation loops, quality measurement, LLM-as-judge, and failure analysis
- Experience with frontier topics such as multimodal models, agents, tool use, test-time compute, and world models
- Published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI
- PhD in ML/NLP or equivalent practical experience
- Public work such as substantial AI side projects, interdisciplinary experiments, or open-source contributions
- Full-stack research ownership experience
- Professional fluency in written and spoken English
Responsibilities
- Research and develop state-of-the-art foundation models
- Optimize realtime inference
- Design and build evaluation loops and quality measurement systems
- Perform failure analysis on models and systems
- Frame research questions, run experiments, write results, and ship outcomes
- Collaborate daily with US-based leadership and engineering teams
