AI Research Engineer (Fine-tuning)

Bitfinex; Dubai; On-site; USD 120,000 - 180,000; Full time

Job summary

A leading fintech company is seeking an AI Research Engineer in Dubai to innovate fine-tuning methodologies for advanced models. You’ll drive cutting-edge AI performance through research and implementation while working in a dynamic and collaborative environment.

Qualifications

• Deep expertise in large language model architectures.
• Hands-on experience with large-scale fine-tuning experiments.
• Strong understanding of advanced fine-tuning methodologies.

Responsibilities

• Develop and implement new state-of-the-art fine-tuning methodologies.
• Build, run, and monitor controlled fine-tuning experiments.
• Collaborate with cross-functional teams to deploy fine-tuned models.

Skills

Fine-tuning methodologies

Artificial intelligence

Machine learning

Problem-solving

Collaborative working

Education

PhD in NLP

Degree in Computer Science

Tools

PyTorch

Hugging Face libraries

Job description

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AI Research Engineer (Fine-tuning), Dubai

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Posted:

Expiry Date:

21.08.2025

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Job Description:

Join Tether and Shape the Future of Digital Finance

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

Innovate with Tether

Tether Finance: Our innovative product suite features the world’s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.

But that’s just the beginning:

Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.

Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.

Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.

Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Why Join Us?

Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

Are you ready to be part of the future?

About the job:

As a member of the AI model team, you will drive innovation in supervised fine-tuning methodologies for advanced models. Your work will refine pre-trained models so that they deliver enhanced intelligence, optimized performance, and domain-specific capabilities designed for real-world challenges. You will work on a wide spectrum of systems, ranging from streamlined, resource-efficient models that run on limited hardware to complex multi-modal architectures that integrate data such as text, images, and audio.

We expect you to have deep expertise in large language model architectures and substantial experience in fine-tuning optimization. You will adopt a hands-on, research-driven approach to developing, testing, and implementing new fine-tuning techniques and algorithms. Your responsibilities include curating specialized data, strengthening baseline performance, and identifying as well as resolving bottlenecks in the fine-tuning process. The goal is to unlock superior domain-adapted AI performance and push the limits of what these models can achieve.

Responsibilities:

Develop and implement new state-of-the-art and novel fine-tuning methodologies for pre-trained models with clear performance targets.

Build, run, and monitor controlled fine-tuning experiments while tracking key performance indicators. Document iterative results and compare against benchmark datasets.

Identify and process high-quality datasets tailored to specific domains. Set measurable criteria to ensure that data curation positively impacts model performance in fine-tuning tasks.

Systematically debug and optimize the fine-tuning process by analyzing computational and model performance metrics.

Collaborate with cross-functional teams to deploy fine-tuned models into production pipelines. Define clear success metrics and ensure continuous monitoring for improvements and domain adaptation.

A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).

Hands-on experience with large-scale fine-tuning experiments, where your contributions have led to measurable improvements in domain-specific model performance.

Deep understanding of advanced fine-tuning methodologies, including state-of-the-art modifications for transformer architectures as well as alternative approaches. Your expertise should emphasize techniques that enhance model intelligence, efficiency, and scalability within fine-tuning workflows.

Strong expertise in PyTorch and Hugging Face libraries with practical experience in developing fine-tuning pipelines, continuously adapting models to new data, and deploying these refined models in production on target platforms.

Demonstrated ability to apply empirical research to overcome fine-tuning bottlenecks. You should be comfortable designing evaluation frameworks and iterating on algorithmic improvements to continuously push the boundaries of fine-tuned AI performance.