At a Glance
- Tasks: Drive innovation in AI model architecture and enhance multi-modal systems.
- Company: Join Tether, a leader in the fintech revolution with a global remote team.
- Benefits: Work remotely, collaborate with top minds, and contribute to groundbreaking projects.
- Other info: Dynamic environment with opportunities for impactful contributions and career growth.
- Why this job: Be at the forefront of AI advancements and shape the future of digital finance.
- Qualifications: PhD in NLP or Machine Learning, with hands-on experience in large-scale AI R&D.
The predicted salary is between 40500 - 49500 £ per year.
Join Tether and shape the future of digital finance. At Tether, we’re pioneering a global financial revolution with cutting-edge solutions that empower businesses to seamlessly integrate reserve-backed tokens across blockchains. Transparency is the bedrock of everything we do, ensuring trust in every transaction.
About the job
As a member of the AI model team, you will drive innovation in architecture development for cutting-edge models of various scales, including small, large, and multi-modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field.
You will have a deep expertise in Large Language Model (LLM) and Multi-Modal architectures, a strong grasp of pre-training optimization, and a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements.
- Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers equipped with multi-nodes & thousands of NVIDIA GPUs.
- Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers.
- Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets.
- Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies.
- Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency and performance.
- System Scalability: Contribute to the advancement of distributed training systems.
A degree in Computer Science or related field is required, ideally a PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D. Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training runs is essential, along with familiarity with large-scale, distributed training frameworks, libraries, and tools. Strong expertise in PyTorch and Hugging Face libraries is also required.
AI Research Engineer (Pre-training - LLM & Multi-Modal) - 100% Remote Worldwide employer: Tether
Tether is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for an Edge AI Inference Engineer. With a strong emphasis on employee growth, Tether offers numerous opportunities for professional development and encourages team members to push the boundaries of AI technology in a supportive environment. Located in a vibrant tech hub, employees benefit from a dynamic work atmosphere and access to cutting-edge resources, ensuring that your contributions have a meaningful impact on real-world applications.
StudySmarter Expert Advice🤫
We think this is how you could land AI Research Engineer (Pre-training - LLM & Multi-Modal) - 100% Remote Worldwide
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Tether.
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We think you need these skills to ace AI Research Engineer (Pre-training - LLM & Multi-Modal) - 100% Remote Worldwide
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Tether, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Tether. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Tether
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Tether!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
