Applied AI Engineer – Voice – Enterprise
| Company | xAI |
|---|---|
| Location | Palo Alto, CA, USA, San Francisco, CA, USA |
| Salary | $180000 – $440000 |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level, Senior |
Requirements
- Strong engineering background.
- Experience interfacing between technical and customer-facing teams.
- Excellent verbal and written communication skills in English.
- Ability to translate business and voice-specific product needs into technical solutions.
- Proven experience implementing voice AI or machine learning products, including APIs, back-end, and front-end voice interfaces.
- Strong proficiency in Python and/or TypeScript.
- Solid understanding of HTTP protocol and real-time communication protocols (e.g., WebRTC).
Responsibilities
- Designing and building end-to-end Voice AI solutions, from understanding customer pain points to scoping product specs and deploying LLM-powered voice interfaces.
- Benchmarking voice models, writing evaluations, or analyzing performance to identify weaknesses in speech recognition, synthesis, or natural language understanding.
- Improving model performance through system prompt tuning, fine-tuning voice-specific models, or optimizing for low-latency voice interactions.
- Analyzing voice request logs, prompt data, or audio inputs to enhance system accuracy and user experience.
- Building internal tools to automate voice AI workflows, such as transcription pipelines or real-time voice processing.
- Enhancing xAI’s Voice AI SDKs or developer documentation based on customer feedback and enterprise use cases.
Preferred Qualifications
- Building evaluations for voice AI capabilities, such as speech recognition accuracy or naturalness of synthesized speech.
- Demonstrating expertise in machine learning fundamentals, including voice model evaluation, training, or fine-tuning.
- Deploying voice AI models to production, optimizing for low-latency and high-reliability environments.
- Writing developer documentation or creating voice-specific SDKs.
- Working with large-scale audio datasets, optimizing voice processing pipelines, or scaling systems for enterprise-grade workloads.
- Using infrastructure tools like Pulumi or Terraform for deploying voice AI systems.