Voice AI Platform Engineer: Real-Time Inference & Ownership

BaseTen; New York, San Francisco (NY, CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive pay

Full coverage benefits

Flexible PTO

Parental leave

Fertility stipend

401(k)

Learning opportunities

Job summary

Baseten is building a production-grade Voice AI platform and you will own Baseten Voice AI from roadmap to production. You’ll work with Forward Deployed Engineers and sister teams to push the boundaries of real-time model serving for STT, TTS, and voice agents.

This high-impact role requires leadership across architecture, implementation, and long-term production operations, with a strong emphasis on collaboration and delivering reliable, scalable AI infrastructure.

Qualifications

• Bachelor's degree in Computer Science or related field.
• Proven track record owning production-grade real-time, large-scale systems where tail latency matters.
• Proficient in one or more programming languages; Python proficiency is a plus.
• Strong product sense for developer-oriented tools and ML infrastructure.
• Interest in ML/AI infrastructure and willingness to learn.

Responsibilities

• Own Voice AI product areas end-to-end from architecture to production operations.
• Design, build, and operate real-time model serving systems for STT, TTS, and voice workloads.
• Coordinate cross-team collaboration to align priorities and deliver end-to-end solutions.
• Mentor teammates through code reviews and design discussions.

Skills

Production systems

Python

Dev tooling

Collaboration

ML infrastructure

Communication

AI assistants usage

Education

Bachelor’s degree in CS

Tools

Docker

Kubernetes

TensorRT

vLLM

Job description

Baseten is building a production-grade Voice AI platform and you will own Baseten Voice AI from roadmap to production. You’ll work with Forward Deployed Engineers and sister teams to push the boundaries of real-time model serving for STT, TTS, and voice agents.

This high-impact role requires leadership across architecture, implementation, and long-term production operations, with a strong emphasis on collaboration and delivering reliable, scalable AI infrastructure.