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- Tasks: Develop evaluation frameworks for AI models and track performance metrics.
- Company: Join Tether, a leader in digital finance and blockchain innovation.
- Benefits: Enjoy remote work flexibility and be part of a global team.
- Why this job: Contribute to cutting-edge fintech projects that shape the future of finance.
- Qualifications: Degree in Computer Science; PhD preferred, with strong AI model evaluation experience.
- Other info: Collaborate with diverse teams and enhance your skills in a dynamic environment.
The predicted salary is between 36000 - 60000 £ per year.
Join Tether and shape the future of digital finance. At Tether, we're pioneering a global financial revolution with innovative solutions that enable seamless integration of reserve-backed tokens across blockchains. Our offerings include the trusted stablecoin USDT, energy solutions for Bitcoin mining, data sharing apps like KEET, and educational initiatives to democratise digital learning. We are a global, remote team looking for passionate individuals to contribute to our cutting-edge projects. If you have excellent English communication skills and a drive to innovate in fintech, Tether is the place for you.
About the job: As part of our AI model team, you will develop evaluation frameworks for pre-training, post-training, and inference stages. Your focus will be on designing metrics and assessment strategies to ensure models are responsive, efficient, and reliable across various applications, from resource-limited devices to multi-modal architectures. You should have expertise in advanced model architectures, evaluation practices, and benchmarking. Your work will involve developing novel evaluation strategies, testing, and implementing them to track performance indicators such as accuracy, latency, throughput, and memory footprint. Collaboration with cross-functional teams to share findings and improve deployment strategies will be key.
Responsibilities:
- Develop and deploy evaluation frameworks assessing models at all stages, tracking KPIs like accuracy, latency, and memory usage.
- Create high-quality datasets and benchmarks to reliably measure model robustness and improvements.
- Collaborate with product, engineering, and operations teams to align evaluation metrics with business goals, presenting insights via dashboards and reports.
- Analyze evaluation data to identify bottlenecks and propose optimisations for model performance and resource utilisation.
- Conduct research to refine evaluation methodologies, staying updated on emerging techniques to enhance benchmarking and model reliability.
Minimum requirements:
- A degree in Computer Science or related field; PhD in NLP, Machine Learning, or similar is preferred, with a strong publication record in top conferences.
- Experience in designing and evaluating AI models across different stages, with proficiency in developing assessment frameworks.
- Strong programming skills and experience building scalable evaluation pipelines; familiarity with performance metrics like latency, throughput, and memory footprint.
- Ability to conduct iterative experiments and research to improve evaluation practices.
- Experience collaborating with diverse teams and translating technical insights into actionable recommendations.
AI Research Engineer (Model Evaluation) employer: Tether Operations Limited
Contact Detail:
Tether Operations Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Research Engineer (Model Evaluation)
✨Tip Number 1
Familiarise yourself with the latest trends in AI model evaluation. Read up on recent publications and case studies that focus on innovative evaluation frameworks, as this will help you speak knowledgeably during interviews.
✨Tip Number 2
Network with professionals in the fintech and AI research communities. Attend relevant webinars or conferences to connect with industry experts, which can provide insights into Tether's work and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your previous projects in detail, especially those involving model evaluation and performance metrics. Be ready to explain your thought process and the impact of your work on model efficiency and reliability.
✨Tip Number 4
Showcase your collaborative skills by highlighting experiences where you've worked with cross-functional teams. Emphasise how you translated technical insights into actionable recommendations, as this aligns with Tether's emphasis on teamwork.
We think you need these skills to ace AI Research Engineer (Model Evaluation)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI model evaluation, programming skills, and any publications in top conferences. Use keywords from the job description to align your qualifications with Tether's requirements.
Craft a Compelling Cover Letter: In your cover letter, express your passion for fintech and how your expertise in AI aligns with Tether's mission. Mention specific projects or experiences that demonstrate your ability to develop evaluation frameworks and collaborate with cross-functional teams.
Showcase Your Technical Skills: Include examples of your work with advanced model architectures and evaluation practices. If possible, provide links to projects or papers that showcase your ability to design metrics and assessment strategies.
Prepare for Potential Interviews: Anticipate questions related to your experience with AI models and evaluation methodologies. Be ready to discuss your approach to developing KPIs and how you would tackle challenges in model performance and resource utilization.
How to prepare for a job interview at Tether Operations Limited
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with AI models, particularly in designing and evaluating them. Highlight any specific frameworks or methodologies you've developed, and be ready to explain how they align with the responsibilities outlined in the job description.
✨Demonstrate Collaboration Skills
Since the role involves working with cross-functional teams, share examples of past collaborations. Discuss how you effectively communicated technical insights to non-technical stakeholders and how this led to successful project outcomes.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to identify bottlenecks and propose optimisations. Think of specific scenarios where you improved model performance or resource utilisation, and be ready to walk through your thought process.
✨Stay Updated on Industry Trends
Research the latest advancements in AI evaluation methodologies and benchmarking techniques. Being knowledgeable about emerging trends will not only impress your interviewers but also demonstrate your commitment to continuous learning in the field.
