Research Scientist - Audio

Stealth Startup; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Stealth Startup is seeking a founding Machine Learning Research Engineer to advance real-time AI. You will explore state-of-the-art approaches across LLMs, speech models, and multimodal AI to build human-like voice agents for complex environments.

Design evaluation methodologies, prototype innovative systems, and improve reasoning, latency, and conversational intelligence. Your research will influence production systems and bridge cutting-edge ML with large-scale deployment.

Qualifications

• Strong ML research background in LLM pre-training and post-training.
• Expertise in speech models and ASR.
• Solid foundation in modern ML architectures, math, and algorithms.

Responsibilities

• Research and develop techniques across LLMs, speech models, and multimodal AI.
• Improve reasoning, latency, and conversational quality.
• Explore new training paradigms, architectures, and inference techniques.
• Design, train, and iterate on ML models and supporting pipelines.
• Rapidly prototype ideas and validate them through experiments.
• Optimize models for production performance and scalability.
• Create evaluation frameworks and datasets to reflect real-world interactions.

Skills

ML research

LLM

ASR

Deep learning

Mathematics

Education

Master's degree in CS/AI/ML

PhD preferred

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.