Research Scientist - Audio

Stealth Startup; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Stealth Startup is seeking a Founding Machine Learning Research Engineer to push the boundaries of real-time AI. You will explore LLMs, speech models, and multimodal AI to build voice agents capable of operating in complex environments.

You will design evaluation methodologies, prototype systems, and improve reasoning, latency, and conversational intelligence with direct impact on production-scale deployments.

Qualifications

• Strong ML research background in areas like LLMs and ASR.
• Proficiency with PyTorch and deep learning theory.
• Advanced mathematics foundational to ML.
• Master's degree is required; PhD preferred or equivalent experience.

Responsibilities

• Research and develop novel techniques across LLMs, speech, and multimodal AI.
• Improve reasoning, latency, and conversational quality in systems.
• Design evaluation frameworks and datasets reflecting real-world use.
• Prototype models and pipelines; validate ideas through experiments.
• Collaborate with engineering to deploy research into production.

Skills

LLM pre-training

Speech recognition

Multimodal AI

Python

PyTorch

Reinforcement learning with human反馈

Education

Master's degree in Computer Science or related field

PhD preferred or equivalent research experience

Tools

PyTorch

Job description

This is a research-driven, high-impact opportunity for machine learning researchers passionate about advancing real-time AI.

As a Founding Machine Learning Research Engineer, you'll explore state-of-the-art approaches across large language models (LLMs), speech models, and multimodal AI to build human-like voice agents capable of operating in complex real-world environments.

You'll design new evaluation methodologies, prototype innovative systems, and improve reasoning, latency, and conversational intelligence. Your research will directly influence production systems, bridging cutting-edge machine learning research with large-scale deployment.

If you enjoy solving open-ended ML challenges, experimenting rapidly, and building AI systems used by thousands of customers, this role offers a unique opportunity to shape the future of conversational AI.

Key Responsibilities

Research & Experimentation

• Research and develop novel techniques across LLMs, speech models, and multimodal AI.
• Improve reasoning capabilities, response latency, conversational quality, and system robustness.
• Explore new training paradigms, architectures, and inference techniques.
• Design, train, and iterate on machine learning models and supporting pipelines.
• Rapidly prototype research ideas and validate them through experimentation.
• Optimize models for production performance and scalability.
• Design innovative evaluation frameworks for conversational AI.
• Create datasets, benchmarks, and metrics that reflect real-world customer interactions.
• Measure model quality across latency, reasoning, speech quality, and user satisfaction.

Production Impact

• Partner closely with engineering teams to deploy research innovations into production.
• Optimize models for reliability, scalability, and operational efficiency.

Human Feedback Systems

• Develop methodologies that incorporate human evaluation into model training.
• Improve conversational quality through reinforcement learning and human feedback.
• Build feedback loops that continuously enhance AI performance.
• Stay current with cutting-edge ML research.
• Evaluate emerging techniques and incorporate them into production systems.
• Contribute to the long-term technical direction of AI infrastructure.

Minimum Qualifications

• Strong machine learning research background in areas such as:
• LLM pre-training and post-training
• Speech recognition (ASR)
• Deep understanding of modern machine learning architectures and algorithms.
• Strong proficiency with PyTorch.
• Excellent mathematical foundation in machine learning and deep learning.
• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field required.
• PhD preferred, or equivalent research-level industry experience.