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- Tasks: Design and optimise algorithms for AI models that align with human preferences.
- Company: Leading AI research firm based in Cambridge or London.
- Benefits: Hybrid working, competitive salary, and opportunities for publishing research.
- Why this job: Join a cutting-edge team shaping the future of AI with real-world impact.
- Qualifications: PhD in relevant field and expertise in Reinforcement Learning and generative models.
- Other info: Collaborative environment with chances to publish in top-tier conferences.
The predicted salary is between 36000 - 60000 £ per year.
This is a permanent position with candidates required to do hybrid working in either Cambridge or London.
Our client are looking for AI Researchers specialising in Reinforcement Learning with Human Feedback (RLHF) and Generative AI. In this role, you will design and optimise the algorithms that align large-scale generative models with human preferences, ensuring they are safe, controllable, and capable of producing high-quality outputs across multiple modalities. You’ll sit at the intersection of RL, LLMs, and generative modelling, helping us build the next generation of foundation models.
Responsibilities:
- Develop and refine RLHF algorithms for large language and generative models.
- Research and implement deep reinforcement learning methods (policy gradients, actor-critic, off-policy learning) for model alignment.
- Train, fine-tune, and evaluate LLMs and diffusion models at scale.
- Design experiments to align generative outputs with human and organisational preferences.
- Collaborate with researchers, engineers, and human feedback teams to build scalable alignment pipelines.
- Publish findings in top-tier AI conferences and contribute to open-source frameworks.
Key Requirements:
- PhD in Computer Science, Machine Learning, or related field.
- Publications at NeurIPS, ICML, ICLR, ACL, or related venues.
- Deep expertise in Reinforcement Learning (policy optimisation, reward modelling, RLHF).
- Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs).
- Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow).
- Proficiency in Python and standard ML libraries.
- Solid foundations in probability, optimisation, and statistics.
- Experience working with large-scale distributed training on GPUs/TPUs.
If this sounds of interest, please reach out to daniel@microtech-global.com.
Senior Researcher, Machine Learning employer: Microtech Global Ltd
Contact Detail:
Microtech Global Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Researcher, Machine Learning
✨Network Like a Pro
Get out there and connect with folks in the AI and machine learning community. Attend meetups, conferences, or webinars where you can chat with industry experts. You never know who might have a lead on your dream job!
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those related to RLHF and generative models. Share your findings from any research papers you've published. This will help us see your expertise in action and make you stand out!
✨Ace the Interview
Prepare for technical interviews by brushing up on deep reinforcement learning methods and algorithms. Practice explaining complex concepts clearly and concisely. Remember, we want to see how you think and solve problems, so be ready to showcase your thought process!
✨Apply Through Our Website
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows us you’re genuinely interested in joining our team and contributing to cutting-edge AI research.
We think you need these skills to ace Senior Researcher, Machine Learning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Senior Researcher in Machine Learning. Highlight your experience with RLHF and generative models, and don’t forget to mention any relevant publications. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI research and how your background makes you a perfect fit for this role. Be sure to mention specific projects or experiences that relate to the responsibilities listed in the job description.
Showcase Your Research Impact: When detailing your research experience, focus on the impact of your work. Mention any algorithms you’ve developed or optimised, and how they contributed to advancements in the field. We love seeing how your contributions have made a difference!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it’s super easy to do!
How to prepare for a job interview at Microtech Global Ltd
✨Know Your Algorithms
Make sure you brush up on your knowledge of RLHF algorithms and deep reinforcement learning methods. Be ready to discuss specific techniques like policy gradients and actor-critic methods, as well as how you've applied them in past projects.
✨Showcase Your Research
Prepare to talk about your publications and any relevant research you've conducted. Highlight your contributions to top-tier AI conferences and how they relate to the role. This will demonstrate your expertise and commitment to the field.
✨Demonstrate Collaboration Skills
Since this role involves working with various teams, be prepared to share examples of successful collaborations. Discuss how you've worked with engineers and human feedback teams to build scalable alignment pipelines in previous roles.
✨Familiarise Yourself with Tools
Ensure you're comfortable discussing the deep learning frameworks mentioned in the job description, like PyTorch, JAX, and TensorFlow. If you have experience with large-scale distributed training on GPUs/TPUs, be ready to explain your approach and any challenges you faced.
