Applied AI ML Associate - Machine Learning Scientist - Machine Learning for Technology
- Research and explore new machine learning methods through independent study, attending conferences, and experimentation.
- Develop state-of-the-art machine learning models to solve real-world problems in Cybersecurity, Software, and Technology Infrastructure.
- Collaborate with partner teams to deploy solutions into production.
- Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models.
- Contribute to reusable code and components shared internally and externally.
- PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) or an MS with industry or research experience.
- Hands-on experience and solid understanding of machine learning and deep learning methods.
- Extensive experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
- Scientific thinking and the ability to invent.
- Ability to design experiments and training frameworks, and evaluate metrics for model performance aligned with business goals.
- Experience with big data and scalable model training.
- Solid written and spoken communication to effectively communicate technical concepts and results.
- Curious, hardworking, detail-oriented, and motivated by complex analytical problems.
- Ability to work both independently and in collaborative team environments.
- Experience with A/B experimentation and data/metric-driven product development.
- Experience with cloud-native deployment in a large-scale distributed environment.
- Knowledge of large language models (LLMs) and accompanying toolsets (e.g., Langchain, Vector databases, open-source Hugging Face Models).
- Knowledge in Reinforcement Learning or Meta Learning.
- Published research in areas of Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal.
- Ability to develop and debug production-quality code.
- Familiarity with continuous integration models and unit test development.
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