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

McLean, Virginia, United States

Pay
Pay not listedSimilar roles state $165K–$240K
Work setting
On-site — work setting source
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.
Read the full posting
Employment
Full-time — employment source
Employment type in the employer’s posting: Full-Time
From the employer’s posting
Apply at Falls Technology

What you’ll work on

Full posting
  • Test, evaluate and validate LLM-based AI systems, including RAG testing

  • Design and run experiments that benchmark LLMs against defined measures

  • Tune neural networks or LLMs on custom datasets for concrete use cases

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

  • Communicate methodology and model results to technical and non-technical stakeholders

From the employer’s posting
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)
Communicate methodological choices and model results to technical and non-technical stakeholders

What you’ll bring

All qualifications

Core experience

  • Active TS/SCI clearance and U.S. citizenship
  • Hands-on RAG testing experience
  • PyTorch, TensorFlow or Keras
  • GPU-accelerated computing
  • Experience testing or validating LLM-based AI systems
Qualification wording
Active TS/SCI required. U.S. citizenship required.
Hands-on RAG testing experience (must-have)
PyTorch, TensorFlow or Keras (key)
GPU-accelerated computing (key)
Testing, evaluating or validating LLM-based AI systems (key)

Tools in this posting

  • Python
  • R
  • SQL
  • Tableau
  • Flask
  • Hugging Face
  • Keras
  • PyTorch
  • scikit-learn
  • TensorFlow
  • Transformers
Source — Tool mentions in context
- 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
- 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)
- 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
- Experiment design to benchmark LLMs (key) - SQL including CTEs, set ops, aggregates and nested subqueries - GitHub and Jenkins (or equivalent) version control / CI
- Semantic search applications - Tableau dashboards - Verification/validation test benches, Explainable AI (XAI), ONNX
- Cloud computing development and architecture - Flask or similar front-end/web frameworks - Text classification and topic modeling (scikit-learn or deep learning)
Nice to have - Hugging Face Transformers library and hub - Building benchmarks that measure LLM performance
- 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
- Building AI/ML models and ensembles in Python or R - PyTorch, TensorFlow or Keras (key) - GPU-accelerated computing (key)
- Flask or similar front-end/web frameworks - Text classification and topic modeling (scikit-learn or deep learning) - Semantic search applications

Posting record

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Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 28, 2026 · 2 days ago
Recorded sightings
3
Last seen by us
Oct 30, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

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  • Ask the employer about the pay range before committing time to the process.

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Job description

View original posting ↗

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.


Source details

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Pay

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Location & working pattern

McLean, Virginia, United States

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.
Work authorization
Active TS/SCI required. U.S. citizenship required. Location: On-site in McLean, VA (Full-Time).

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