AI Model Quality Assurance Engineer
2 weeks ago
Join a pioneering global financial revolution with innovative solutions that enable seamless integration of reserve-backed tokens across blockchains.
- We offer cutting-edge data sharing and AI solutions like KEET, digital learning initiatives for individuals in the digital and gig economies, and innovating at the intersection of technology and human potential.
Our remote, global team is passionate about fintech innovation. If you excel in English communication and want to contribute to a groundbreaking platform, this is the place for you.
About the Job:You will develop evaluation frameworks and benchmark methodologies for pre-training, post-training, and inference of AI models. Your focus will be on designing metrics and assessment strategies to ensure models are responsive, efficient, and reliable across various applications and hardware environments.
Key Responsibilities:- Develop and deploy evaluation frameworks assessing models at all stages, tracking key performance indicators such as accuracy, latency, throughput, and memory footprint.
- Create evaluation datasets and benchmarks to measure model robustness and improvements.
- Collaborate with product, engineering, and data teams to align evaluation metrics with business goals, presenting insights through dashboards and reports.
- Analyze evaluation data to identify bottlenecks, propose optimizations, and enhance model performance and scalability.
- Refine evaluation methodologies through experiments and research, staying updated on emerging techniques.
- Degree in Computer Science or related field; PhD in NLP, Machine Learning, or similar is preferred.
- Proven experience in designing and evaluating AI models across different lifecycle stages.
- Strong programming skills and experience with evaluation benchmarks, pipelines, and performance metrics.
- Ability to conduct iterative experiments and stay current with emerging trends.
- Experience working with cross-functional teams and translating technical insights into actionable recommendations.
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