Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED Ready to push the boundaries of natural language processing and AI? In this role, you will be at the forefront of automated language processing, tackling complex natural language data from both spoken and written origins. You won't just run off-the-shelf models—you’ll architect autonomous tokenization pipelines, build automated part-of-speech annotation solutions, and rigorously score and benchmark model performance against human-annotated baselines. If you thrive on pushing the boundaries of NLP, fine-tuning machine learning models, and bringing cutting-edge AI/LLM capabilities to high-stakes missions, you’ll fit right in with Team Nyla. The annual base salary range for this role is $152,000-$180,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. • Python Expertise: Advanced, production-level proficiency in Python for data manipulation, algorithmic modeling, and NLP tool development. • Natural Language Processing (NLP): Extensive hands-on experience designing and deploying core NLP workflows, including tokenization, text normalization, and parts-of-speech (POS) tagging. • Model Evaluation & Scoring: Demonstrated track record of benchmarking machine learning/NLP models, performing error analysis, and measuring accuracy against human-annotated datasets. • Multi-Modal Language Processing: Practical experience processing diverse natural language datasets originating from both spoken (transcribed) and written sources. Education: Bachelor’s Degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, PLUS 7+ years of professional experience in data science, NLP, or software engineering OR Master’s Degree in a related field, PLUS 5+ years of relevant professional experience OR High School Diploma / GED , PLUS 11+ years of specialized technical experience in lieu of a degree. AI & Large Language Models (LLMs): Hands-on experience testing, fine-tuning, or integrating modern LLMs and generative AI frameworks into production analytical pipelines. Machine Learning & Frameworks: Familiarity with modern ML libraries and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face, spaCy, NLTK). MDLA & Domain Experience: Prior exposure to Massively Distributed Language Processing Frameworks (MDLA) or specialized language processing architectures. AI Testing & Evaluation: Practical experience constructing evaluation testbeds and testing strategies for emerging AI/ML techniques.

Requirements

Must-haves

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
Python Expertise: Advanced, production-level proficiency in Python for data manipulation, algorithmic modeling, and NLP tool development.
Natural Language Processing (NLP): Extensive hands-on experience designing and deploying core NLP workflows, including tokenization, text normalization, and parts-of-speech (POS) tagging.
Model Evaluation & Scoring: Demonstrated track record of benchmarking machine learning/NLP models, performing error analysis, and measuring accuracy against human-annotated datasets.
Multi-Modal Language Processing: Practical experience processing diverse natural language datasets originating from both spoken (transcribed) and written sources.
AI & Large Language Models (LLMs): Hands-on experience testing, fine-tuning, or integrating modern LLMs and generative AI frameworks into production analytical pipelines.
Machine Learning & Frameworks: Familiarity with modern ML libraries and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face, spaCy, NLTK).
MDLA & Domain Experience: Prior exposure to Massively Distributed Language Processing Frameworks (MDLA) or specialized language processing architectures.
AI Testing & Evaluation: Practical experience constructing evaluation testbeds and testing strategies for emerging AI/ML techniques.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimachine learningllmpythonml

Platform: agile-ats
Effort: medium