Uber Senior AI/ML Engineer 

United States, West Virginia 

307953079

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .

- - - - What the Candidate Will Do ----

1. Develop Security Solutions: designing, developing, and implementing software solutions to enhance the security posture of the organization.
2. Develop and evaluate large-scale machine learning models systems
3. Research: Researching new techniques and tools to enhance the organization's cyber defense capabilities.
4. Present findings to business and executive audiences
5. Collaborate with engineers and product managers to implement ideas and plan future roadmaps
6. Optimize retrieval-augmented generation (RAG) systems for enhanced performance and relevance.
7. Fine-tune large language models (LLMs) to improve predictive accuracy and operational efficiency.
8. Implement agentic workflows to streamline processes and enhance decision-making.
9. Collaboration and Communication: Work closely with cross-functional teams such as IAM, network operations, incident response, and compliance to implement ideas, plan future roadmaps and ensure a cohesive approach to cybersecurity.

- - - - Basic Qualifications ----

1. Ph.D., MS or Bachelors degree in Statistics, Operations Research, Computer Science, Engineering, or other quantitative field
2. 5+ years of industry experience in software engineering
3. Knowledge of underlying mathematical foundations of machine learning, statistics, optimization, economics, and analytics
4. Hands-on experience building and deployment ML models.
5. Knowledge of experimental design and analysis
6. Ability to use a language like Python or R to work efficiently at scale with large data sets

- - - - Preferred Qualifications ----

1. Knowledge in modern machine learning techniques applicable to Cybersecurity domain
2. Proficiency in technologies in one or more of the following: SQL, Spark, Hadoop
3. Advanced understanding of statistics, causal inference, and machine learning
4. Experience designing and analyzing large scale online experiments
5. Experience working with large scale data sets using technologies like Hive, Presto, and Spark
6. Experience with synthetic data generation.
7. Proficiency in fine-tuning and optimizing large language models (LLMs).
8. Experience in retrieval-augmented generation (RAG) systems.
9. Familiarity with agentic workflows and their applications in machine learning and AI systems.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .