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.