Senior Machine Learning Engineer

Nyla Technology Solutions•Annapolis Junction, MD

About The Position

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.

Requirements

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
Advanced, production-level proficiency in Python for data manipulation, algorithmic modeling, and NLP tool development.
Extensive hands-on experience designing and deploying core NLP workflows, including tokenization, text normalization, and parts-of-speech (POS) tagging.
Demonstrated track record of benchmarking machine learning/NLP models, performing error analysis, and measuring accuracy against human-annotated datasets.
Practical experience processing diverse natural language datasets originating from both spoken (transcribed) and written sources.
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.

Nice To Haves

Hands-on experience testing, fine-tuning, or integrating modern LLMs and generative AI frameworks into production analytical pipelines.
Familiarity with modern ML libraries and deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face, spaCy, NLTK).
Prior exposure to Massively Distributed Language Processing Frameworks (MDLA) or specialized language processing architectures.
Practical experience constructing evaluation testbeds and testing strategies for emerging AI/ML techniques.

Responsibilities

Architect autonomous tokenization pipelines.
Build automated part-of-speech annotation solutions.
Rigorously score and benchmark model performance against human-annotated baselines.
Push the boundaries of NLP.
Fine-tune machine learning models.
Bring cutting-edge AI/LLM capabilities to high-stakes missions.