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- Tasks: Develop and benchmark ML algorithms on cutting-edge hardware and tackle complex problems.
- Company: Pioneering deep-tech organisation leading the way in AI research.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Startup mentality with a focus on innovation and collaboration.
- Why this job: Join a high-impact team and shape the future of generative AI.
- Qualifications: Strong ML algorithm experience and proficiency in Python/PyTorch required.
The predicted salary is between 36000 - 60000 £ per year.
A pioneering deep-tech organisation in the UK seeks a Machine Learning Research Engineer to develop and benchmark ML algorithms on novel computing hardware. The role involves working on generative models and translating complex problems into ML solutions.
The ideal candidate has strong experience in ML algorithms, excellent Python/PyTorch skills, and a Master's or PhD in a relevant field.
Join a high-impact team at the forefront of AI research with a startup mentality.
ML Research Engineer: Next-Gen Hardware & Generative AI in London employer: IntaPeople Limited
Contact Detail:
IntaPeople Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Research Engineer: Next-Gen Hardware & Generative AI in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that dream role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving generative models. We want to see your Python and PyTorch prowess in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your algorithms and problem-solving skills. We recommend practicing coding challenges and discussing your thought process out loud, as this can really impress interviewers.
✨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!
We think you need these skills to ace ML Research Engineer: Next-Gen Hardware & Generative AI in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with ML algorithms and Python/PyTorch in your application. We want to see how your skills align with the role, so don’t hold back!
Tailor Your Application: Take a moment to customise your CV and cover letter for this specific role. Mention your experience with generative models and any relevant projects you've worked on. It helps us see why you’re a great fit!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your past experiences and how they relate to the job. We appreciate clarity and directness!
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Don’t miss out!
How to prepare for a job interview at IntaPeople Limited
✨Know Your Algorithms
Make sure you brush up on the latest ML algorithms, especially those related to generative models. Be ready to discuss how you've applied these in past projects and how they can be benchmarked on novel hardware.
✨Show Off Your Python Skills
Since Python and PyTorch are key for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice coding challenges that involve ML concepts.
✨Understand the Hardware
Familiarise yourself with the types of novel computing hardware that are currently being used in ML research. Being able to discuss how different hardware can impact algorithm performance will show your depth of understanding.
✨Prepare for Problem-Solving Questions
Expect to tackle complex problems during the interview. Think about how you would translate real-world issues into ML solutions and be ready to walk through your thought process clearly and logically.