Senior Machine Learning Engineer

Location: San Francisco, CA (Onsite)

Position: Full-Time

Company Overview

This company is a San Francisco-based leader in enterprise voice AI, building a platform that allows businesses to create sophisticated, human-like AI phone agents at scale. The mission is to fundamentally transform customer interactions through technology. The team is rapidly growing, backed by $65 million in funding from top-tier investors including Emergence Capital, Scale Venture Partners, Y Combinator, and the founders of iconic companies like Twilio and Affirm.

The Role: Senior Machine Learning Engineer

The company is seeking a seasoned Machine Learning Engineer to own the intelligence core of its voice AI platform. This role is central to ensuring that the AI agents are not just functional, but genuinely human-like in their conversational quality. The individual in this position will architect, build, and scale the ML systems that directly drive real-world business outcomes for enterprise customers, making the critical difference between a robotic interaction and a natural conversation.

Key Responsibilities

1. Own the End-to-End ML Stack: Lead the engineering and optimization of self-hosted speech-to-text (STT), large language model (LLM), and text-to-speech (TTS) systems, taking them from research and prototyping to robust production deployment.
2. Architect Production-Grade Inference Systems: Design and implement high-throughput, low-latency ML infrastructure capable of serving millions of daily voice interactions with sub-second latency requirements.
3. Drive Conversational Quality: Research and implement novel techniques to continuously improve the agents' conversational abilities, enhance retrieval-augmented generation (RAG) pipelines, and reduce overall response latency.
4. Optimize for Enterprise Scale: Tackle complex inference challenges, including model quantization, efficient serving architectures, and cost-performance trade-offs for large-scale customer deployments.
5. Collaborate for Real-World Impact: Work closely with Deployment Engineers to understand customer requirements and translate business needs into practical, effective ML solutions that perform reliably in production environments.
6. Pioneer New Capabilities: Experiment with cutting-edge research in conversational AI, real-time speech processing, and multi-modal understanding to maintain the platform's position at the forefront of voice technology.

Ideal Candidate Profile

The ideal candidate is a specialist with deep, hands-on experience in building and scaling mission-critical ML systems. They thrive on solving complex engineering challenges and are driven by a obsession for quality and performance.

Required Qualifications:

1. 3+ years of professional experience in machine learning, with at least 1 year focused on speech, conversational AI, or a closely related field, and a proven record of shipping ML systems to production.
2. Demonstrable experience with TTS/STT systems and a passion for implementing novel solutions in this domain.
3. A specialist's depth; the ability to focus intensely on a specific problem within the STT/LLM/TTS stack and master it completely.
4. Proven experience building and scaling ML infrastructure from the ground up (0 to 1) and through significant growth (1 to 100), understanding the full lifecycle from research to enterprise-scale deployment.
5. A full-stack ML mindset, with comfort working across the entire pipeline: data, training, inference, and monitoring.
6. Startup DNA, with proven success in fast-moving environments where owning outcomes and navigating ambiguity are paramount.

Preferred Qualifications (Bonus Points):

1. Experience with real-time speech processing pipelines or telephony systems.
2. Background in large-scale distributed training and inference.
3. Experience building conversational AI, chatbots, or voice assistants.
4. A PhD in Machine Learning, AI, or equivalent deep research experience.

How You Show Up

The successful candidate will embody the following traits:

1. Ownership Mindset: You take end-to-end responsibility for your systems' performance and reliability, proactively solving problems without waiting for direction.
2. Quality-Obsessed: You are deeply committed to the craft, ensuring that every interaction sounds truly human and not like a robotic phone tree.
3. Data-Driven: You base decisions on rigorous measurement and experimentation, letting empirical results guide the path forward.
4. Collaborative: You work seamlessly with cross-functional teams to deliver solutions that meet real customer needs and work reliably in production.
5. Relentless: You persevere through ambiguous and complex technical challenges, driving until you find a robust solution.

Compensation and Benefits

The company offers a competitive salary and a meaningful equity stake in a fast-growing company. The benefits package includes comprehensive healthcare (medical, dental, vision) and all the necessary tools and resources to succeed. The role is based in a beautiful office in San Francisco's Jackson Square neighborhood.