Founding Machine Learning Engineer

Machine Learning Engineer - Founding Team Stealth AI Startup (Audio + Generative Models)

Location: London (Hybrid) Full-time Competitive Salary + Equity

A well-funded, early-stage startup backed by top-tier investors is seeking an ambitious Machine Learning Engineer to join as their first full-time ML hire.

As a core member of the founding team, you'll generative voice and speech-to-speech models and your work will directly shape the company's core products and have a real impact on users.

The ideal candidate is a builder at heart-someone who's either been a founder or has shipped impressive side projects-and is excited to work in a fast-paced, high-performance environment.

What You'll Do

• Design and implement cost-efficient, high-performance infrastructure for storing and transforming massive audio datasets.
• Apply ML audio and DSP techniques to clean, segment, and filter speech data.
• Manage large-scale cloud data storage with a deep understanding of cost-performance tradeoffs.
• Build scalable ML training pipelines in PyTorch using large datasets.
• Contribute to research and development of generative voice and speech-to-speech models.
• Prototype and implement novel ML/statistical approaches to enhance product capabilities.
• Develop robust testing pipelines to evaluate model performance on audio data.

What We're Looking For

• PhD in a relevant field (e.g., Deep Generative Models, TTS, ASR, NLU), or equivalent industry experience.
• Deep expertise in voice conversion, generative models, deep learning, or statistical modeling.
• Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras).
• Proficiency in Python and C/C++.
• Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow).
• Proven ability to manage GPU-intensive data processing jobs.
• 4+ years of applied research or industry experience.
• Creative problem-solver with a bias for action and a passion for building world-class products.
• Excellent communication skills.

Bonus Points

• Extensive experience in applied research, especially in voice conversion, speech synthesis, or NLP.
• PhD specialization in voice or speech-related ML fields.
• A track record of thought leadership through publications, open-source contributions, or patents.