Multimodal AI Model Optimization Research Engineer

Remote · Full-time · Remote · Posted Sep 28, 2026

At a glance

At Tavus, we're building the human layer of AI. Our mission is to make human-AI interaction as natural as face-to-face interaction, enabling the human touch where it has been previously unscalable.
We achieve this through pioneering research in multimodal AI for modeling human-to-human communication (language, audio, and video), as well as generating audio-visual avatar behavior. Our models power everything from text-to-video AI avatars to real-time conversational video experiences across industries like healthcare, recruiting, sales, and education.
By enabling AI to see, hear, and communicate with human-like authenticity, we're creating the foundation for the next generation of AI employees, assistants, and companions.
We are a Series B company backed by top investors, including Sequoia, Y Combinator, and Scale VC. Join us in driving the future of human-AI interaction.

Tavus is seeking an experienced Research Scientist/Engineer to join their core AI team with a focus on model optimization.
The role involves taking cutting-edge research models and making them fast, efficient, and production-ready.
The engineer will own the optimization lifecycle, including defining metrics, running experiments, and benchmarking trade-offs.

Take cutting-edge research models and make them fast, efficient, and production-ready using sparsification, distillation, and quantization.
Own the optimization lifecycle for key models.
Define metrics, run experiments, and benchmark trade-offs across latency, cost, and quality.
Partner closely with researchers and engineers to turn new ideas into deployable systems.

Strong experience in deep learning using PyTorch.
Hands-on experience with model optimization and compression, including knowledge distillation, pruning/sparsification, quantization, and mixed precision.
Understanding of efficient architectures such as low-rank adapters.
Strong understanding of inference performance and GPU/accelerator fundamentals.
Strong Python coding skills and reliable research engineering practices.
Experience working with large models and datasets in cloud environments.
Ability to read ML papers, reproduce results, and adapt ideas.
Clear communication and collaboration skills.

Optimization of diffusion models, video/audio generative models, or large language models.
Experience with real-time or streaming systems (low-latency APIs, WebRTC, streaming TTS/video).
Familiarity with TensorRT, ONNX Runtime, TVM, Triton, or XLA.
Experience writing custom Triton/CUDA kernels or low-level performance tuning.
Experience with experiment tracking, benchmarking, and profiling at scale.
Prior experience in research engineering or applied science roles.