Leidos QTC Health Services is hiring a Data Scientist Intern . The position requires general machine learning and artificial intelligence knowledge so that the candidate can contribute to various machine learning (ML) and artificial intelligence (AI) projects in the Leidos QTC Health Services AI/ML Accelerator. Candidates must be familiar with handling multiple types of data (text, image, video, audio, lidar, etc..), primarily related to abstract reasoning especially mitigating for incomplete, misrepresented, and incorrect reasoning in diagrammatic reasoning. The candidate should have hands-on knowledge of 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 QTC 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.
Primary Responsibilities
Create AI/ML prototypes for use cases requiring identifying entity/objects, determining object association, object disambiguation, anomaly detection, state estimations, etc.
Develop and maintain data models (both physical and logical)
Perform extraction, transform, and load (ETL) tasks related to the different modalities and algorithms being applied. This data ETL includes identifying the datas relevant metadata to ensure consistency, quality, accuracy, integrity, and information assurance and security.
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 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
Pursuing Masters or PhD 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 security clearance
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
Practical hands-on experience and the ability to explain statistical analysis, reinforcement learning, transfer learning, natural language processing, and computer vision
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/or Text
Preferred Qualifications
Visualizations/Web Development Skills (e.g. Tableau, D3, etc.).
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