Senior ML Engineer (Enterprise Voice AI Platform)

Company Overview

We are an enterprise software company based in San Francisco, building a platform that enables businesses to deploy and manage AI-powered voice agents. The company is venture-backed, having raised significant capital from established firms including Emergence Capital and Scale Venture Partners, as well as experienced technology founders. Our team is focused on solving practical infrastructure challenges for enterprise customer service and operations.

Position: Senior ML Engineer

We are hiring a Senior ML Engineer to develop and maintain the core machine learning systems powering our voice AI platform. Your primary responsibility will be to ensure these systems are reliable, scalable, and performant for business use cases. This role focuses on the engineering and optimization of production ML infrastructure, directly impacting the technical quality and operational efficiency of customer-facing voice agents.

Responsibilities

1. Lead the development, optimization, and deployment of self-hosted speech-to-text, large language model, and text-to-speech systems.
2. Design, build, and maintain high-throughput inference infrastructure capable of serving millions of daily interactions with strict latency requirements.
3. Implement improvements to model performance, including enhancements to conversational quality, retrieval-augmented generation pipelines, and latency reduction.
4. Address scaling challenges specific to enterprise deployments, such as model quantization, efficient serving architectures, and cost management for large-scale inference.
5. Collaborate with deployment engineers to translate customer requirements and feedback into functional ML system improvements.
6. Conduct methodical experimentation with emerging techniques in conversational AI and speech processing to address specific platform needs.

Qualifications

1. 3+ years of professional experience in machine learning, with at least 1 year focused on speech or conversational AI systems.
2. Hands-on experience with the practical aspects of TTS (Text-to-Speech) and/or STT (Speech-to-Text) system implementation and optimization.
3. Demonstrated ability to take ownership of complex, specific technical problems within the ML stack and drive them to resolution.
4. Proven experience in building and scaling ML infrastructure from prototype to production, understanding the requirements for system reliability and scale.
5. Comfort working across the ML pipeline, including data handling, training, inference, and performance monitoring.
6. Experience operating in an environment with shifting priorities and ambiguous requirements, where self-direction is necessary.

Preferred Experience

1. Background in real-time speech processing or telephony systems.
2. Experience with large-scale distributed model training and inference.
3. Previous work on conversational AI, chatbots, or voice assistant products.
4. Advanced degree (PhD) in Machine Learning, AI, or a related field, or equivalent research experience.

Working Style

1. You take clear ownership of your systems' performance and operational health.
2. You are detail-oriented and focused on the incremental improvements that affect system output quality.
3. You base decisions on measurement, experimentation, and observable results.
4. You work effectively with cross-functional engineering and deployment teams to deliver integrated solutions.
5. You are persistent in diagnosing and solving complex, ambiguous technical problems.

Our Stack

You will be working with a modern ML stack, including self-hosted STT, LLM, and TTS systems. The role involves optimizing distributed inference infrastructure and integrating with telephony and data platforms.