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For Companies At a Glance
- Tasks: Join a founding team to design and implement cutting-edge ML models for audio applications.
- Company: Be part of a well-funded stealth AI startup backed by top-tier investors.
- Benefits: Enjoy a competitive salary, equity options, and a hybrid work environment.
- Why this job: Shape innovative products and make a real impact in a fast-paced, high-performance culture.
- Qualifications: PhD or equivalent experience in relevant fields with strong ML framework skills required.
- Other info: Ideal for builders with a passion for creating world-class products.
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 generate 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 that may not be advertised.
✨Tip Number 2
Showcase your projects on platforms like GitHub or personal websites. Highlight any relevant side projects or contributions to open-source initiatives that demonstrate your skills in machine learning and audio processing. This will give potential employers a tangible sense of your capabilities.
✨Tip Number 3
Engage with the startup ecosystem by following and interacting with relevant companies and thought leaders on social media. This can keep you informed about industry trends and job openings, and it shows your enthusiasm for the field.
✨Tip Number 4
Prepare to discuss your problem-solving approach during interviews. Be ready to share specific examples of challenges you've faced in previous projects and how you overcame them, particularly in areas related to machine learning and audio data.
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 that demonstrate your ability to design and implement ML infrastructure.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for building innovative products and your experience in fast-paced environments. Mention specific projects that align with the job description, especially those involving voice conversion or deep learning.
Showcase Your Projects: Include links to any side projects, publications, or open-source contributions related to machine learning. This will help demonstrate your hands-on experience and creativity in the field.
Highlight Relevant Skills: Clearly list your technical skills, such as proficiency in Python, C/C++, and ML frameworks like PyTorch and TensorFlow. Make sure to mention any experience with scalable data tools, 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 values. Be ready to explain how your background and expertise align with their goals, particularly in shaping core products that impact users.
✨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 ready to answer technical questions or even solve problems on the spot.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your creative problem-solving abilities. Think of examples where you've had to innovate or adapt quickly in a high-performance environment, and be ready to share those experiences.
