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.