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

McLean, Virginia, United States

Actively hiring, first seen by us 6 days ago

Pay: Pay not listedSimilar roles state $166K–$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

Before you apply

Work authorization: Citizens onlyActive TS/SCI required

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)

Apply at Falls Technology

Posting record

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Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 28, 20266 days ago
Recorded sightings: 8
Last seen by us: Nov 4, 2026

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

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 6

Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Falls Technology is hiring

4 open jobs

Open jobs by the week we first saw them, last 12 weeks.

ML Engineer hiring is picking up

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
R
SQL
Tableau
Flask
Hugging Face
Keras
PyTorch
scikit-learn
TensorFlow
Transformers

Where the posting names them

- 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

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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.

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