
Building Efficient AI Architectures
3 days ago
As a key member of our team, you will play a crucial role in developing evaluation frameworks for pre-training, post-training, and inference. Your work will focus on metrics such as accuracy, latency, and memory usage, spanning resource-efficient models to complex multi-modal architectures.
Key responsibilities include:
- Designing and deploying evaluation frameworks that assess models at all lifecycle stages.
- Creating datasets and benchmarks for measuring model robustness and quality.
- Collaborating with cross-functional teams to align evaluation metrics with business goals and present insights via dashboards.
- Analyzing evaluation data to optimize model performance and resource utilization.
- Refining evaluation methodologies through iterative research and staying updated on emerging techniques.
Required skills and qualifications include:
- A degree in Computer Science or a related field; PhD preferred, with a record of AI R&D and publications.
- Experience designing and evaluating AI models across lifecycle stages, with proficiency in evaluation frameworks.
- Strong programming skills and familiarity with benchmarking pipelines, performance metrics, and resource optimization.
- Ability to conduct experiments, research, and stay current with new techniques.
- Experience working with cross-functional teams, translating technical insights into actionable strategies.
Benefits of this position include:
The opportunity to work with a talented team of experts in the field of artificial intelligence.
Access to cutting-edge technology and resources.
Professional development opportunities, including training and mentorship.
A dynamic and inclusive work environment.
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