Founding Applied ML Engineer Audio & Speech

Location: San Francisco (strongly preferred) or New York City

Position Type: Full-Time (Permanent)

Start Timeline: ASAP

Hiring Target: 2

About

A fast-scaling early-stage startup is building the worlds most advanced audio dataset infrastructure global, studio-grade, and optimized for ML training. The team is leveraging audio to bridge digital and real-world AI applications, with early traction from major tech labs and customers.

Why join

Founding team role with high ownership and early equity

Lead ML architecture for cutting-edge speech models

Fast-paced ML environment (75% engineering / 25% research)

Significant impact across research, infra, and production deployment

Paid meals, strong benefits, and 401(k) access

Compensation

• Base Salary: $200,000 $250,000
• Equity: Competitive (~0.5%)
• Relocation: Required (must be onsite 5 days/week in SF or NY)
• Visa Sponsorship: Available
• Tech environment
• Languages: Python
• Frameworks: PyTorch
• Cloud: AWS or GCP
• Domain: DSP, Audio ML, Generative AI

Role summary: As a Founding Applied ML Engineer, youll 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 core ML models.

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 GCP)
• 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

Preferred qualifications

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