Machine Learning Engineer

Dezign Concepts LLC•Mclean, VA

16d

About The Position

The Sponsor seeks an experienced Machine Learning Engineer to develop and adapt Large Language Models (LLMs) and Natural Language Processing (NLP) methodologies using AI/ML frameworks and programming languages. The role builds tools and analytics using structured and unstructured Sponsor data to generate analytic insights, accelerate workflows, and help analysts surface relevant information from large data stores. Work is performed within a team environment and requires an existing active Top Secret clearance with Polygraph. The engineer will leverage open-source models to create reusable models and ensembles through techniques such as fine-tuning and transfer learning. Work spans classification, prediction, sensor- and entity-level fusion, decision-making, and generative AI, as well as NLP, deep learning, recommender systems, threat detection, computer vision, data mining, and statistics. The role also analyzes and prepares data, develops ETL code and table configurations, uses SQL, documents methods in GitHub, contributes to publications and reports, and provides technical education and AI/ML expertise to Sponsor staff. Local travel using a personal vehicle may be needed on an as-needed basis within the local place of performance. Salary is up to $190,000 and is commensurate with education and experience.

Requirements

Must be a U.S. citizen.
Must hold an active Top Secret SCI clearance with Polygraph.
Minimum bachelor’s degree required.
Expert-level experience required.
Demonstrated experience tuning neural networks, such as LLMs, on custom data sets and applying results to specific use cases.
Professional or academic experience using Python or R.
Professional or academic experience developing AI/ML models and ensembles, including selecting Python libraries, choosing preprocessing actions, performing analysis, and assessing model performance.
Professional or academic experience testing, evaluating, or validating AI system performance, with a focus on LLM-based systems.
Professional or academic experience designing and executing experiments to test LLM performance against a benchmark.
Professional or academic experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras.
Professional or academic proficiency with SQL, including common table expressions, set operations, aggregate functions, and nested subqueries.
Experience leveraging GPUs for accelerated computing.
Professional or academic experience with version control systems such as GitHub and Jenkins.

Nice To Haves

Professional or academic experience with the Hugging Face Transformers library and hub.
Experience with cloud computing development and architecture.
Experience with front-end web development frameworks such as Flask.
Experience creating Python machine-learning models for text classification and topic modeling using standard machine-learning libraries such as scikit-learn or deep-learning models.
Experience developing applications for semantic search.
Professional or academic proficiency with Tableau for producing visualizations and dashboards.
Academic or professional experience communicating methodological choices and model results.
Experience with verification and validation test benches.
Experience with Explainable AI (XAI) techniques.
Experience with ONNX (Open Neural Network Exchange).

Responsibilities

Leverage open-source models to create reusable models and ensembles using fine-tuning, transfer learning, and other methods.
Develop models and capabilities for classification, prediction, sensor- and entity-level fusion, decision-making, and generative AI.
Work across machine learning, AI, deep learning, NLP, recommender systems and personalization, threat detection, computer vision, data mining, and statistics.
Analyze large volumes of raw data, including text, to determine its utility for training or testing AI models.
Preprocess and clean structured and unstructured Sponsor data, including text data.
Design and implement advanced ETL code and table configurations for complex data sets.
Use SQL to develop and organize relevant information.
Work cooperatively with a team; author analytic publications and ad-hoc reports, including data visualizations using Sponsor templates; and document methods in GitHub.
Implement the Sponsor’s existing coordination process.
Provide technical education to staff on an ad-hoc basis.
Provide AI/ML subject matter expertise to support Sponsor initiatives.