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For Companies At a Glance
- Tasks: Join a pioneering team to develop cutting-edge voice and speech models.
- Company: Be part of a well-funded stealth AI startup in London, backed by top-tier investors.
- Benefits: Enjoy a competitive salary, equity options, and a hybrid work environment.
- Why this job: Shape innovative products that impact users while working in a fast-paced, creative atmosphere.
- Qualifications: PhD or equivalent experience in ML, with expertise in voice conversion and deep learning.
- Other info: Ideal for builders eager to make a mark in the AI industry.
The predicted salary is between 48000 - 84000 £ per year.
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 work on 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.
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.
Founding Machine Learning Engineer employer: JR United Kingdom
Contact Detail:
JR United Kingdom Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding Machine Learning Engineer
✨Tip Number 1
Network with professionals in the AI and machine learning community. Attend meetups, webinars, or conferences related to generative models and audio processing. This can help you make valuable connections and learn about opportunities directly from insiders.
✨Tip Number 2
Showcase your projects on platforms like GitHub or personal websites. Highlight any side projects or contributions to open-source that demonstrate your skills in machine learning, especially in audio and speech technologies. This will give potential employers a tangible sense of your capabilities.
✨Tip Number 3
Engage with the startup ecosystem by following relevant blogs, podcasts, and social media channels. Understanding the latest trends and challenges in the AI space can help you tailor your conversations during interviews and show your enthusiasm for the field.
✨Tip Number 4
Prepare to discuss your problem-solving approach in detail. Be ready to share specific examples of how you've tackled complex challenges in previous roles or projects, particularly those involving machine learning and audio data. This will demonstrate your hands-on experience and creative thinking.
We think you need these skills to ace Founding Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly in audio and generative models. Emphasise any projects or roles where you've designed and implemented ML infrastructure or worked with large datasets.
Craft a Compelling Cover Letter: In your cover letter, express your passion for building innovative products and your excitement about joining a founding team. Mention specific experiences that demonstrate your problem-solving skills and ability to work in fast-paced environments.
Showcase Your Projects: If you have side projects or contributions to open-source related to voice conversion or generative models, include them in your application. This will showcase your hands-on experience and creativity in the field.
Highlight Relevant Skills: Clearly list your technical skills, especially in Python, PyTorch, and any experience with scalable data tools. Make sure to mention your familiarity with GPU-intensive data processing, as this is crucial for the role.
How to prepare for a job interview at JR United Kingdom
✨Showcase Your Projects
Be prepared to discuss any impressive side projects or previous work that demonstrates your skills in machine learning, especially in audio and generative models. Highlight specific challenges you faced and how you overcame them.
✨Understand the Company’s Vision
Research the startup's mission and products thoroughly. Be ready to articulate how your expertise aligns with their goals and how you can contribute to shaping their core offerings.
✨Demonstrate Technical Proficiency
Brush up on your knowledge of ML frameworks like PyTorch and TensorFlow, as well as your programming skills in Python and C/C++. Be prepared to solve technical problems or answer questions related to these technologies during the interview.
✨Prepare for Problem-Solving Questions
Expect to face scenario-based questions that assess your problem-solving abilities. Think about how you would approach designing scalable ML training pipelines or managing large datasets, and be ready to explain your thought process.
