We’re looking for an LLM Research Scientist Intern. This intern will assist in the development and maintenance of interactive AI solutions. Collaborate with the AI, UI/UX, and development teams to identify the most effective user engagement to support the AI interactivity and user workflow. The position requires general machine learning and artificial intelligence knowledge of chatbots, virtual assistants, and recommendation engines. Additionally, the candidate will train LLMs to conduct summaries and respond to end-user prompts. The candidate should be familiar with creating models that understand user prompts and give dynamic responses based on the chain of thought or tree of thought as in a conversation. The candidate must be familiar with AI models that automatically understand and shift context based on user interaction flow. 

The training data will be instruction data that guides people in conducting AI governance. The candidate will train an LLM on the instructional material to illicit key points, descriptions, and summaries that instructional designers will combine to create instructional courses. Candidates must be experienced with handling multiple types of data (Text, image, video, audio, lidar, etc..), primarily related to large language models (LLMs)/Generative AI and using transformers. The candidates must understand the theoretics of reinforcement learning, enabling continuous learning and task optimization. Ethical AI is part of all Leidos AI/ML projects.

Consequently, we want candidates to know the concepts of Ethical AI and how to apply them. Along with those skills, the candidate must have demonstrated the ability to work independently and in technical teams to implement and customize algorithms to fuse multiple data modalities. In this position at Leidos Arlington, VA. the candidate should have at least intermediate Python coder ability and hands-on experience using ML libraries like SciKit Learn, DKube, KubeFlow, Feast, Azure, TensorFlow, Keras, etc. The candidates’ knowledge should also include experience containerizing AI models and using the containers with AWS, Microsoft Azure, or Google Cloud.

Primary Responsibilities:

• Build reusable, testable, and efficient code for front-end and back-end systems.
• Develop, deploy, and manage AI/ML models, including Generative AI models (GPT, DALL-E, etc.), to solve business problems.
• Work with data scientists to integrate AI/ML models into production environments.
• Fine-tune models, manage version control, and monitor performance in production systems.
• Develop and maintain CI/CD pipelines to automate model deployment and web application releases.
•  Implement DevOps best practices for infrastructure as code (IaC) using tools like Docker, Kubernetes, and Terraform.
• Conduct anomaly detection using various AI/ML techniques
• Properly configure general adversarial networks on their own and in conjunction with training such as Generative AI
• Understanding of autoencoders and their parameters
• Engineer prompts for LLMs and Generative AI
• Use algorithms to identify complex patterns across multiple modalities
• Increase the efficiency and quality of data alignment and fusion
• Design rewards for reward model training in reinforcement learning
• Enhance and maintain analysis tools, including automation of current processes using AI/ML algorithms quantitative data analysis, including developing retrieval, processing, fusion, analysis, and visualization of various datasets
• Configure and program prototypes Jupyter notebooks with ML solutions
• Setup and use AWS instances to train and operate AI/ML models

Basic Qualifications:

• College students actively seeking a B.S. degree in Aerospace Engineering, Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or related fields
• Must be able to obtain a Top-Secret security clearance with a polygraph
• US citizenship required
• Knowledge of Deep Learning Frameworks such as Keras, Tensorflow, PyTorch, mxnet, etc. - Ability to apply these frameworks to real problems in the ’time --series’ domain
• Assist in the development and testing of commercial web applications
• Collaborate with senior developers on various software development projects
• Apply user-centered design principles in web application development
• Participate in agile development processes and team meetings
• Contribute to the improvement of existing software and the creation of new features
• Intermediate software development skills lifecycle including developing and maintaining good production quality code
• Hands-on Software Development Skills (Python-Preferred)
• Experience or educational courses/projects in Machine Learning and Text

Preferred Qualifications:

• Visualizations/Web Development Skills (e.g., Tableau, D3).
• Hands-on experience with prototype development
• Hands-on experience with automating data cleansing, formatting, staging, and transforming data human
• Hands-on experience applying data analytics
• Hands-on experience with prompt engineering
• Hands-on experience with reinforcement learning
• Hands-on experience with LLMs and Generative AI
• Hands-on experience with intelligent systems and machine learning
• Experience with the interpretability of deep learning models
• Big Data Skills (Azure, Hadoop, Spark, recent deep learning platforms)
• Experience with text mining tools and techniques, including in areas of summarization, search (e.g., ELK Stack), entity extraction, training set generation (e.g., Snorkel), and anomaly detection
• Hands-on experience with DKube
• Hands-on experience with KubeFlow
• Hands-on experience with Feast

Original Posting Date:

2024-12-03

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

Pay Range $44,850.00 - $81,075.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.