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 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 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 data’s 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 PhD degree in Aerospace Engineering, Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or related fields

• US Citizenship required

• 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.).

• 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 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:

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:

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.