Job Description
Work on use cases that include but are not limited to threat detection, policy recommendation, LLM-powered applications, malware detection, content classification, anomaly detection, and AIOps (advanced networking diagnosis). These various use cases give you the opportunity and challenges to experience multiple machine learning algorithms and tools. You will design and create machine learning models to provide faster and more effective ways to address cloud security, cloud operations, and cloud intelligence use cases.
You might not be an expert in every stage of a Machine Learning project, but you should have interests and curiosity in various parts ranging from the data to the model’s business impact.
Qualifications
- 4+ years of experience as a Machine Learning Engineer or Data Scientist
- Experience with Python (pandas, sklearn, pytorch) and SQL
- Experience with feature engineering, model evaluation and model error analysis
- Experience with Large Language Models (LLMs)
- Passion for leveraging ML/AI to solve real-world business problems (better security and better business results)
- Master's Degree in Computer Science/Engineering required, data science concentration is a plus; PhD is preferred
- Good business sense and customer obsession
- Solid computer science foundation
Additional Preferred Skills
- Prompt engineering and fine tuning Large Language Models (LLMs)
- Experience with various program languages such as (Py)Spark.
- Graph Neural Networks/Knowledge Graphs
- Experience with unsupervised learning (clustering), and evaluating unsupervised models
- Experience with active/few-shot learning
- Experience with logs analysis/logs mining
- Research Experience/Publications
- Experience with various public cloud services (such as AWS, Google, Azure) and ML automation platforms (such as Kubeflow).
- Good understanding of operating systems and distributed systems.
- Familiarity with networking and networking security .