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- Tasks: Develop innovative machine learning algorithms for next-gen computing technologies.
- Company: Pioneering organisation at the forefront of computing advancements.
- Benefits: Collaborative environment, cutting-edge projects, and opportunities for research-driven growth.
- Other info: Ideal for those passionate about research and developing new ML methods.
- Why this job: Join a team blending machine learning, physics, and advanced computation to make a real impact.
- Qualifications: Strong background in ML research, algorithm development, and proficiency in PyTorch and Python.
The predicted salary is between 36000 - 60000 ÂŁ per year.
We are partnering exclusively with a pioneering organisation developing next‑generation computing technologies. This is a rare opportunity to join a team working at the intersection of machine learning, computational science, and emerging hybrid computing architectures. The work focuses on algorithmic innovation, generative modelling, and benchmarking ML systems on cutting‑edge hardware platforms.
You will contribute to the development of new machine learning algorithms designed for advanced computing systems that extend beyond traditional GPU/TPU environments. The role involves close collaboration with researchers across ML, physics, and scientific computing, with a strong emphasis on generative models, numerical methods, and hybrid quantum‑classical approaches.
Essential Experience
- Experience with heterogeneous computing hardware (HPC, NPUs, ASICs, quantum systems).
- Strong background in machine learning research, including algorithm development and benchmarking.
- Hands‑on experience with generative models (diffusion, flows, GANs).
- Proficiency in PyTorch, Python, and modern ML tooling.
- Experience in computational science, numerical methods, or scientific computing.
- MSc or PhD in Machine Learning, Computer Science, Physics, Applied Mathematics, or a related field, or equivalent research experience.
This role is ideal for candidates who thrive in research‑driven environments, enjoy developing new ML methods, and want to work on problems that blend machine learning, physics, and advanced computation. Due to the nature of the work, this role is best suited to candidates with a strong research background rather than purely production‑focused ML experience. If you’re excited by the idea of building ML algorithms for non‑traditional compute architectures, we’d welcome a confidential conversation.
Machine Learning Research Scientist / Research Engineer employer: IntaPeople
Contact Detail:
IntaPeople Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Research Scientist / Research Engineer
✨Tip Number 1
Network like a pro! Reach out to professionals in the machine learning and computational science fields on platforms like LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving generative models or algorithm development. Use GitHub to share your code and document your thought process. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of heterogeneous computing hardware and modern ML tooling. Practice coding challenges and be ready to discuss your past research experiences in detail. We want to see how you think and solve problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search. So, get your application in and let’s start a conversation about your future in machine learning!
We think you need these skills to ace Machine Learning Research Scientist / Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with heterogeneous computing hardware and machine learning research. We want to see how your background aligns with the role, so don’t be shy about showcasing your skills in algorithm development and generative models!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning and how your research experience makes you a perfect fit for our team. Let us know what excites you about working at the intersection of ML and advanced computation.
Showcase Relevant Projects: If you've worked on any projects involving PyTorch, Python, or generative models, make sure to include them in your application. We love seeing practical examples of your work, especially if they relate to the cutting-edge technologies we’re developing!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it shows us you’re keen to join our innovative team!
How to prepare for a job interview at IntaPeople
✨Know Your Algorithms
Make sure you brush up on the latest machine learning algorithms, especially those related to generative models like GANs and diffusion models. Be ready to discuss your past experiences with these algorithms and how they can be applied to advanced computing systems.
✨Showcase Your Technical Skills
Be prepared to demonstrate your proficiency in PyTorch and Python. You might be asked to solve a coding problem or explain your approach to benchmarking ML systems. Practising coding challenges beforehand can really help you feel more confident.
✨Understand the Hardware Landscape
Familiarise yourself with heterogeneous computing hardware such as HPC, NPUs, and quantum systems. Being able to discuss how these technologies impact machine learning will show that you’re not just knowledgeable but also genuinely interested in the field.
✨Collaborative Mindset
Since the role involves working closely with researchers from various fields, highlight your teamwork skills. Share examples of past collaborations and how you’ve successfully worked with others to tackle complex problems in machine learning or computational science.