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- Tasks: Join a cutting-edge team to develop LLM Agents for coding and research.
- Company: FAIR, a leader in AI research with a focus on innovation.
- Benefits: Competitive salary, on-site collaboration, and opportunities for groundbreaking research.
- Other info: Dynamic environment with opportunities to lead complex projects.
- Why this job: Make a real impact in the AI field while working with top experts.
- Qualifications: PhD in relevant fields and deep expertise in Python and LLMs.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for a research engineer to join FAIR to work on LLM Agents for coding and research. Will consider candidates who have experience training coding agents and turning LLM agents for code (or at least experience in adjacent areas and desires to learn this field).
Roles & Responsibilities
- Responsible for code generation LLMs, transformers for code, compilers or program analysis, and/or large-scale ML model training.
- The role centers on machine learning for code — including program optimization, verification, code reasoning, and software engineering agents built on LLMs.
- Support creative data sourcing, high-quality pre/mid/post-training data curation, and scale and optimize data pipelines for multimodal large language models (LLMs).
- Responsible for designing architectures to make LLMs better at generalization and reasoning.
Qualifications
- PhD in Computer Science, Mathematics, Physics, or related field, with strong research or industry experience.
- Deep expertise in Python Programming Language.
- Strong experience working with Large Language Models (LLMs).
- Experience with tools and frameworks for building and working with modern AI models (e.g., PyTorch, TensorFlow, Transformers, Hugging Face).
- Proven ability to lead complex and ambiguous technical projects from conception to completion.
ML Engineer employer: Qualitest
Contact Detail:
Qualitest Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the ML community, attend meetups, and connect with researchers on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs and coding agents. This could be anything from GitHub repos to blog posts explaining your work. It’s a great way to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML concepts. Practice coding challenges and be ready to discuss your past projects 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. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the ML Engineer role. Highlight your PhD research and any relevant experience with LLMs, coding agents, or similar projects. We want to see how your background fits 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 machine learning for code and how your skills align with our needs. Be genuine and let us know what excites you about this opportunity.
Showcase Your Projects: If you've worked on any projects related to LLMs or coding agents, make sure to mention them! Include links to your GitHub or any publications. We love seeing practical applications of your skills and creativity.
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the process smoother for both of us!
How to prepare for a job interview at Qualitest
✨Know Your Stuff
Make sure you brush up on your knowledge of LLMs and coding agents. Be ready to discuss your experience with Python, PyTorch, and TensorFlow. They’ll likely ask you about specific projects you've worked on, so have a couple of examples ready that showcase your skills.
✨Show Your Research Skills
Since this role is research-focused, be prepared to talk about your PhD work and how it relates to the position. Highlight any innovative approaches you've taken in your research, especially those involving program optimization or data curation.
✨Prepare for Technical Questions
Expect some technical questions that test your understanding of machine learning concepts and coding practices. Practice explaining complex ideas simply, as they might want to see how well you can communicate technical information to non-experts.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about their current projects involving LLMs or how they approach data sourcing. This shows your genuine interest in the role and helps you gauge if the company is the right fit for you.