AI Research Engineer

Há 6 dias

Brasil Tether Tempo inteiro

AI Research Engineer (Model Evaluation) at Tether.io

About the job: As a member of our AI model team, you will drive innovation across the entire AI lifecycle by developing and implementing rigorous evaluation frameworks and benchmark methodologies for pre-training, post-training, and inference. Your work will focus on designing metrics and assessment strategies that ensure our models are highly responsive, efficient, and reliable across real-world applications. You will work on a wide spectrum of systems, from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate text, images, and audio.

You will:

  • Develop, test, and deploy integrated frameworks that rigorously assess models during pre-training, post-training, and inference. Define and track key performance indicators such as accuracy, loss metrics, latency, throughput, and memory footprint across diverse deployment scenarios.
  • Curate high-quality evaluation datasets and design standardized benchmarks to reliably measure model quality and robustness. Ensure benchmarks reflect improvements from both pre-training and post-training processes, driving consistency in evaluation practices.
  • Engage with product management, engineering, data science, and operations to align evaluation metrics with business objectives. Present evaluation findings and recommendations through dashboards and reports supporting decision-making.
  • Analyze evaluation data to identify bottlenecks across the model lifecycle. Propose and implement optimizations that enhance performance, scalability, and resource utilization on resource-constrained platforms.
  • Conduct iterative experiments and empirical research to refine evaluation methodologies, staying abreast of emerging techniques and trends to improve benchmarking practices and model reliability.
  • Collaborate with cross-functional teams to share evaluation findings and integrate stakeholder feedback. Build robust evaluation pipelines and performance dashboards that drive continuous improvement in model deployment strategies.
Qualifications
  • A degree in Computer Science or related field; ideally PhD in NLP, Machine Learning, or related field, with a solid track record in AI R&D (publications in A* conferences).
  • Experience designing and evaluating AI models across pre-training, post-training, and inference stages. Proficiency in developing evaluation frameworks that assess accuracy, convergence, loss improvements, and robustness.
  • Strong programming skills with hands-on expertise in evaluation benchmarks and frameworks. Experience building, automating, and scaling complex evaluation and benchmarking pipelines. Familiarity with metrics such as latency, throughput, and memory footprint.
  • Proven ability to conduct iterative experiments and empirical research to refine evaluation methodologies and stay updated with emerging trends.
  • Experience collaborating with product, engineering, and operations teams to align evaluation strategies with organizational goals and translate technical findings into actionable insights.

Employment type: Full-time

Job function: Information Technology

Industries: Technology, Information and Internet

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