Staff / Principal Machine Learning Engineer, Serving
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 9/25/2026
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
Take models from the research team, containerize and optimize their serving, and ensure reliable production operation. Work on inference optimization, model acceleration, and high-performance distributed systems powering realtime multimodal AI at scale.
Requirements
- Deep understanding of modern serving frameworks such as vLLM or TRT-LLM
- Hands-on experience with quantization, distillation, caching strategies, continuous batching, paged attention, and speculative decoding
- Proficiency in C++, CUDA, Rust, or highly optimized Python
- Experience with Kubernetes, Ray, custom load balancing, multi-GPU or multi-node inference, and thousands of concurrent connections
- Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups
- Full-cycle ownership from research model to production serving
- PhD in CS, Physics, or Math, or equivalent practical experience building backend or ML systems
- Legal right to work in the United Kingdom
Responsibilities
- Optimize realtime inference and serving frameworks
- Containerize research models and ensure reliable production deployment
- Apply quantization, distillation, caching, continuous batching, paged attention, and speculative decoding
- Profile code and optimize performance on NVIDIA GPUs
- Handle multi-GPU and multi-node inference and thousands of concurrent connections
- Design benchmarks and prototypes to validate technical decisions
- Share work and contribute to open-source projects
Benefits
- Equity
- Benefits