Machine Learning Engineer / SME (LLM Eval & RAG Testing) - SME | TS/SCI

Fallstechnology; McLean (VA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

9% retirement contribution

100% family medical premiums paid

4 weeks PTO

Job summary

Falls Technology in McLean, VA is seeking an on-site AI/ML engineer to design RAG evaluation experiments, tune LLMs on custom data, and run GPU-accelerated benchmarks so the team ships trustworthy AI systems.

The role requires TS/SCI clearance and hands-on experience with PyTorch, TensorFlow, and Keras, plus strong SQL skills for data analysis and reporting to stakeholders.

Qualifications

• Active TS/SCI required.
• Hands-on RAG testing experience is a must.
• Tuning neural nets/LLMs on custom datasets for real use cases.
• Building AI/ML models and ensembles in Python or R.
• Experience with PyTorch, TensorFlow or Keras.
• GPU-accelerated computing experience is essential.
• Testing, evaluating or validating LLM-based AI systems.

Responsibilities

• Test, evaluate and validate AI systems with a focus on LLM-based systems (including RAG testing)
• Design and run experiments that benchmark LLMs against defined measures
• Tune neural networks / LLMs on custom datasets and apply results to concrete use cases
• Build AI/ML models and ensembles in Python or R
• Use deep learning frameworks such as PyTorch, TensorFlow or Keras
• Leverage GPUs for accelerated training and evaluation
• Write solid SQL (CTEs, set operations, aggregates, nested subqueries) against analytical datasets
• Work in GitHub and Jenkins-style CI for reproducible experiments
• Communicate methodological choices and model results to technical and non-technical stakeholders

Skills

RAG testing

Neural network tuning

Model evaluation

Experiment design

SQL proficiency

Stakeholder communication

Education

Tools

PyTorch

TensorFlow

Keras

Python

GitHub

Jenkins CI

Job description

Active TS/SCI required. U.S. citizenship required.

Location: On-site in McLean, VA (Full-Time).

Prove which LLMs actually work for the mission. You'll design RAG and LLM evaluation experiments, tune models on custom data, and run GPU-accelerated benchmarks so the team ships AI systems they can trust.

What you'll do

• Test, evaluate and validate AI systems with a focus on LLM-based systems (including RAG testing)


• Design and run experiments that benchmark LLMs against defined measures


• Tune neural networks / LLMs on custom datasets and apply results to concrete use cases


• Build AI/ML models and ensembles in Python or R (library selection, preprocessing, model assessment)


• Use deep learning frameworks such as PyTorch, TensorFlow or Keras


• Leverage GPUs for accelerated training and evaluation


• Write solid SQL (CTEs, set operations, aggregates, nested subqueries) against analytical datasets


• Work in GitHub and Jenkins-style CI for reproducible experiments


• Communicate methodological choices and model results to technical and non-technical stakeholders

What you bring (required)

• Active TS/SCI


• Hands-on RAG testing experience (must-have)


• Tuning neural nets / LLMs on custom datasets for real use cases


• Building AI/ML models and ensembles in Python or R


• PyTorch, TensorFlow or Keras (key)


• GPU-accelerated computing (key)


• Testing, evaluating or validating LLM-based AI systems (key)


• Experiment design to benchmark LLMs (key)


• SQL including CTEs, set ops, aggregates and nested subqueries


• GitHub and Jenkins (or equivalent) version control / CI

Nice to have

• Hugging Face Transformers library and hub


• Building benchmarks that measure LLM performance


• Cloud computing development and architecture


• Flask or similar front-end/web frameworks


• Text classification and topic modeling (scikit-learn or deep learning)


• Semantic search applications


• Tableau dashboards


• Verification/validation test benches, Explainable AI (XAI), ONNX

Why Falls

A 9% retirement contribution, 100% of family medical premiums paid, and 4 weeks of PTO. We're a small business that's been delivering since 2019, so your work is visible and your voice counts.

Falls Technology is an Equal Opportunity Employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, disability, or protected veteran status.