Applied AI Researcher
4 weeks ago
About the Role
The Data and Applied Scientist will play a pivotal role in developing and implementing cutting-edge AI models in client products and consulting with technical nuances. This position will contribute to the evolution of Azure ML's state-of-the-art LLM modeling capabilities, enabling customers of AzureML to use GenAI.
Key Responsibilities
- Implement and fine-tune neural network architectures, including transformer-based models.
- Optimize model performance, scalability, and efficiency.
- Conduct experiments to evaluate model performance, robustness, and generalization.
- Explore novel techniques and approaches to enhance model capabilities.
- Stay up-to-date with the latest advancements in NLP, deep learning, GenAI, and AI research.
- Work with large-scale datasets, preprocess them, and create appropriate data representations.
- Collaborate with partners across several industries to implement challenging use cases.
- Select relevant features and ensure data quality for training and evaluation.
- Communicate technical findings and insights effectively.
- Deploy trained models in production environments.
- Monitor model performance, troubleshoot issues, and iterate on improvements.
Requirements
- A strong background in machine learning, deep learning, and natural language processing.
- Depth in data science, generative AI, and understanding of data engineering.
- Experience with transformer-based models (e.g., BERT, GPT, T5, Llama).
- Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch).
- Solid understanding of statistics, linear algebra, and probability theory.
- Experience working with structured and unstructured datasets.
- Familiarity with cloud platforms (e.g., Azure) and distributed computing.
- Excellent problem-solving skills and the ability to work independently and collaboratively.
What We Offer
- Industry leading healthcare
- Educational resources
- Discounts on products and services
- Savings and investments
- Maternity and paternity leave
- Generous time away
- Giving programs
- Opportunities to network and connect
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