Why This Job is Featured on The SaaS Jobs
This role stands out in SaaS because it sits at the point where advanced AI capabilities become a shippable product. Tavus is building multimodal systems that combine language, audio, and video, which is increasingly relevant as SaaS applications move beyond text interfaces into real-time, human-like interaction. At a Series B stage, the work implied here tends to be closer to product constraints, not just research novelty, with performance and reliability shaping what customers can actually use.
For a SaaS career, model optimization is a durable specialization: it connects core ML research to unit economics, latency budgets, and deployment realities. Owning metrics, benchmarking trade-offs, and collaborating across research and engineering are recurring patterns in AI-first SaaS teams, especially where inference cost and responsiveness determine product viability. Experience spanning compression techniques and production readiness transfers well across companies shipping large-model features.
The position is best suited to someone who prefers end-to-end ownership and is comfortable making prioritization calls with imperfect information. It will likely appeal to practitioners who enjoy rigorous experimentation, performance tuning, and translating papers into measurable system gains, while working closely with adjacent disciplines to get models into production.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Tavus – Multimodal AI Model Optimization
Research Engineer
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.
The Role
We’re looking for an experienced Research Scientist/Engineer with a focus on model optimization to join our core AI team.
Our ideal partner-in-crime thrives in startup environments, is comfortable prioritizing independently, and is willing to take calculated risks. We’re moving fast and looking for people who can help pave the path.
Your Mission
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
Requirements
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
Preferred Experience
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
Location
This position is preferably hybrid in San Francisco, with relocation support offered. Remote candidates are also considered.
Benefits
When you join Tavus, you’re joining a family. We offer flexible work schedules, unlimited PTO, competitive healthcare and gear stipends, and a collaborative environment focused on learning and impact.
Culture & Diversity
We are not looking for cultural fits — we are looking for culture creators. Diversity drives our success, and we combine varied backgrounds, skills, and perspectives to build the best experiences for our clients..
