Founding Applied ML Engineer Audio & Speech

Role Summary;
As a Founding Applied ML Engineer, you'll own the full ML lifecycle from research and prototyping to inference and resilient deployment. You'll collaborate with Ops to source high-quality training data and drive the roadmap for clients core models.

Tech Stack

• Languages: Python

• Frameworks: PyTorch

• Cloud: AWS or Google Cloud Platform

• Domain: DSP, Audio ML, Generative AI

Key Responsibilities

• Develop cutting-edge ML models for audio and speech

• Implement inference systems and production-ready APIs

• Architect durable pipelines and evaluations

• Translate research into high-performance code

• Drive technical roadmap and cross-functional integration

Must-Have Qualifications

• 5+ years in ML, including 2+ years in audio/speech (DSP/ML)

• Demonstrated ownership of ML systems from POC to production

• Proficient in Python and PyTorch

• Experience with cloud-based ML development (AWS or Google Cloud Platform)

• Prior software engineering experience (~1 year min before ML)

• Ability to connect model quality to user experience and business value

• Strong trajectory (e.g., fast promotions) at top companies

Bonus Points

• Graduate degree (MS or PhD), esp. from top schools like Stanford or MIT

• Stanford Music/Audio Tech program experience

• Led ML teams or drove roadmap direction

• Experience training generative AI models

• Speech research background

• Experience with classical DSP and ML-based audio processing

Prescreening questions (Required for Submission)

1. Tell me about a time you had to build something from scratch. Why?

2. What is your ML experience with a focus on audio, particularly speech? How many years in audio ML experience do you have?