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- Tasks: Design and develop cutting-edge AI models for music creation.
- Company: Join Arthos, a startup revolutionising music with AI-powered tools.
- Benefits: Enjoy a competitive salary, equity, free festival passes, and a personal studio budget.
- Why this job: Be part of a fun, music-driven team pushing creative boundaries in audio technology.
- Qualifications: Expertise in ML, deep learning, and a passion for music is essential.
- Other info: Work in a vibrant environment with flexible hours and relocation support.
The predicted salary is between 72000 - 108000 ÂŁ per year.
Get AI-powered advice on this job and more exclusive features.
Transform how music is made.
Arthos is building software and AI that will create the 10× Hybrid Musician – a human-driven, AI-assisted creative genius. Mozart AI , our first product, is an AI Digital Audio Workstation (DAW) empowering every artist with complete creative control and AI Co-Production. We started 8 weeks ago, raised $1 million from world-class investors, and already have 10k+ beta users making magic. Now we need a research-minded engineer to push generative audio and midi past the limits of imagination.
What you’ll do
Employee No. 2: architect the intelligence behind the best AI DAW. You’ll design, train, and ship state-of-the-art diffusion + transformer models, agentic systems that feel like “trusted ears, ” and turn cutting-edge research into features that change how millions create music.
Must-haves
- Cracked, Outlier – Published in NeurIPS/ICML or built a Kaggle-winning model before senior year? We want you.
- Deep Learning Genius – Diffusion, Transformers, RLHF, 10 000+ hours mastering the craft.
- Agentic AI Expert – Expertise with building and deploying agents
- Production ML Mastery – Distributed training, MLOps, monitoring – model hits prod, scales to millions.
- Math & Stats Wizard – Can derive the ELBO on a whiteboard while the coffee’s still brewing.
- Passion for music – You’ve trained models that sing, mix, or groove, and/or you DJ/ produce for fun.
Nice-to-haves
- Open-source contributions (Magenta, DDSP, Hugging Face, etc.).
- Rust/C++ optimizations for fast inference.
Why you’ll love it
- Base $100k – $150k + 0.5 – 1 % equity (higher for true 10× outliers).
- Free Tomorrowland/Coachella passes every year. Yes VIP is possible too.
- Budget for personal music studio setup.
- Chef-prepped meals , 100 % relocation costs.
- 9 am-1 am warriors welcome and wanted – we live and breathe music + code. And we are very fun folks to be around!
Ready to be Employee No.2?
Email with a 1 liner pitch as to why you, along with your CV + Portfolio.
Seniority level
-
Seniority level
Mid-Senior level
Employment type
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Employment type
Full-time
Job function
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Job function
Engineering and Information Technology
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Industries
Software Development
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Mozart AI - ML Engineer employer: Arthos
Contact Detail:
Arthos Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Mozart AI - ML Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in generative audio and MIDI technologies. Being well-versed in current trends will not only help you during interviews but also demonstrate your genuine passion for the field.
✨Tip Number 2
Engage with the music tech community by attending relevant meetups or online forums. Networking with like-minded individuals can provide valuable insights and potentially lead to referrals, increasing your chances of landing an interview.
✨Tip Number 3
Showcase your personal projects that involve AI in music production. Whether it's a model you've trained or a piece of music you've produced, having tangible examples of your work can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your experience with MLOps and distributed training in detail. Given the role's focus on production ML mastery, being able to articulate your hands-on experience will be crucial during the interview process.
We think you need these skills to ace Mozart AI - ML Engineer
Some tips for your application 🫡
Craft a Compelling One-Liner: Since the application requires a one-liner pitch, make sure it succinctly highlights your unique qualifications and passion for music and AI. This is your chance to grab their attention right from the start!
Showcase Relevant Experience: In your CV and portfolio, emphasise any experience with generative audio, machine learning models, or projects that demonstrate your deep learning expertise. Include specific examples of your work that align with the job description.
Highlight Your Passion for Music: Make sure to convey your love for music in your application. Whether you DJ, produce, or have trained models that create music, share these experiences to show how they connect with the role.
Tailor Your Application: Customise your CV and portfolio to reflect the specific skills and experiences mentioned in the job description. Use keywords like 'Diffusion', 'Transformers', and 'MLOps' to ensure your application resonates with the hiring team.
How to prepare for a job interview at Arthos
✨Showcase Your Passion for Music
Make sure to express your love for music during the interview. Share any personal projects, DJing experiences, or how you've integrated music into your machine learning work. This will demonstrate that you’re not just a techie but also someone who truly understands the creative side of the industry.
✨Prepare for Technical Questions
Given the role's focus on advanced ML techniques like diffusion and transformers, be ready to discuss your experience with these models. Brush up on key concepts and be prepared to explain your past projects, especially those that involved generative audio or MIDI.
✨Highlight Your Research Experience
If you've published in NeurIPS or ICML, make sure to mention it! Discuss your research contributions and how they relate to the role. This will show that you have the depth of knowledge and the innovative mindset that Arthos is looking for.
✨Demonstrate MLOps Knowledge
Since the position requires production ML mastery, be prepared to talk about your experience with distributed training, monitoring, and deploying models. Sharing specific examples of how you've scaled models to handle large user bases will set you apart from other candidates.