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- Tasks: Join us to pre-train cutting-edge AI models and innovate architectures for smarter tech.
- Company: Be part of a leading AI research team pushing the boundaries of technology.
- Benefits: Enjoy flexible work options, competitive pay, and access to the latest tech tools.
- Why this job: Make a real impact in AI while collaborating with top minds in a dynamic environment.
- Qualifications: A degree in Computer Science; PhD preferred, with hands-on experience in AI model training.
- Other info: Work with thousands of NVIDIA GPUs and contribute to groundbreaking AI advancements.
The predicted salary is between 48000 - 84000 £ per year.
Responsibilities:
- Conduct pre-training of AI models on large, distributed servers with thousands of NVIDIA GPUs.
- Design, prototype, and scale innovative architectures to improve model intelligence.
- Execute experiments independently and collaboratively, analyze results, and refine methodologies for optimal performance.
- Investigate, debug, and enhance model efficiency and computational performance.
- Contribute to training systems development to ensure scalability and efficiency on target platforms.
Minimum Qualifications:
- A degree in Computer Science or a related field; a PhD in NLP, Machine Learning, or related fields is preferred, with a strong record in AI R&D and publications in top conferences.
- Hands-on experience with large-scale LLM training on distributed servers with thousands of NVIDIA GPUs.
- Familiarity with large-scale, distributed training frameworks, libraries, and tools.
- Deep knowledge of transformer and non-transformer model modifications to enhance intelligence, efficiency, and scalability.
- Expertise in PyTorch and Hugging Face libraries, with practical experience in model development, pretraining, and deployment.
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AI Research Engineer employer: Bitfinex
Contact Detail:
Bitfinex Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Research Engineer
✨Tip Number 1
Network with professionals in the AI and machine learning community. Attend conferences, webinars, or local meetups to connect with others in the field. This can lead to valuable insights and potential referrals for positions like the AI Research Engineer at StudySmarter.
✨Tip Number 2
Engage in online forums and communities focused on AI research, such as GitHub or Stack Overflow. Share your knowledge, ask questions, and contribute to discussions. This will not only enhance your visibility but also demonstrate your expertise in relevant technologies.
✨Tip Number 3
Work on personal or open-source projects that involve large-scale LLM training and distributed systems. Showcase these projects on your portfolio or GitHub profile to highlight your hands-on experience with the tools and frameworks mentioned in the job description.
✨Tip Number 4
Stay updated with the latest advancements in AI and machine learning by following influential researchers and organisations on social media. This will help you understand current trends and innovations, which you can discuss during interviews to show your passion and knowledge in the field.
We think you need these skills to ace AI Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in AI research, particularly any work with large-scale LLM training and NVIDIA GPUs. Emphasise your educational background, especially if you have a PhD in NLP or Machine Learning.
Craft a Strong Cover Letter: In your cover letter, express your passion for AI research and detail your hands-on experience with distributed training frameworks. Mention specific projects where you've designed or improved model architectures.
Showcase Publications: If you have publications in top conferences, include them in your application. Highlighting your contributions to the field can set you apart from other candidates.
Demonstrate Technical Skills: Clearly outline your expertise in PyTorch and Hugging Face libraries. Provide examples of how you've used these tools in your previous roles to develop, pretrain, or deploy models.
How to prepare for a job interview at Bitfinex
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with large-scale LLM training and the specific frameworks you've used. Highlight any projects where you successfully implemented distributed training on NVIDIA GPUs, as this will demonstrate your capability to handle the technical demands of the role.
✨Discuss Your Research Contributions
Since a strong record in AI R&D is essential, be ready to talk about your publications and any innovative architectures you've designed. Explain how your research has contributed to advancements in model intelligence and efficiency, which will show your depth of knowledge in the field.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your ability to investigate and debug model performance issues. Think of specific examples where you identified inefficiencies and how you enhanced computational performance, as this will illustrate your analytical skills and practical experience.
✨Demonstrate Collaboration Skills
As the role involves executing experiments both independently and collaboratively, be ready to share experiences where you worked effectively within a team. Discuss how you communicated complex ideas and contributed to collective problem-solving, which is crucial for success in a research environment.
