AI Research Engineer (Fine-tuning) Job Tether

AI Research Engineer (Fine-tuning) Job

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.

Job requirements

• 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.

How to Apply

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