Principal Backend Software Engineer

Nyla Technology Solutions•Annapolis Junction, MD

2d•$209,000 - $247,000

About The Position

An active security clearance at the TS/SCI polygraph level is required. Despite the listed title, the description outlines an NLP and data science role leading the design, development, and deployment of advanced machine learning models and computational linguistics pipelines. The role transforms large unstructured text and multimodal datasets into structured intelligence, develops production-grade inference pipelines, and collaborates with cross-functional engineering teams to integrate advanced models into enterprise operational environments. The annual base salary range is $209,000–$247,000 USD, excluding discretionary bonus compensation and benefits. Actual compensation may vary based on geographic location, work experience, education, skill level, and other factors.

Requirements

Active security clearance at the TS/SCI polygraph level.
Hands-on expertise building and fine-tuning neural network architectures using Python and modern ML/NLP frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or spaCy.
Proven experience deploying models into scalable production pipelines using Docker, Kubernetes, and model-serving frameworks such as Triton, TorchServe, or FastAPI.
Advanced proficiency in Python and SQL for vectorization, feature extraction, and manipulation of large structured and unstructured text datasets.
Expertise implementing quantitative evaluation metrics such as BLEU, ROUGE, F1-score, and perplexity, and debugging complex model edge cases.
In-depth understanding of linguistic foundations, semantic analysis, named entity recognition, relation extraction, and embedding techniques.
Bachelor’s degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, plus 14 or more years of professional experience in data science, NLP, or software engineering; alternatively, a high school diploma or GED plus 18 or more years of specialized technical experience in lieu of a degree.

Nice To Haves

Practical experience with parameter-efficient fine-tuning (PEFT/LoRA), retrieval-augmented generation (RAG) frameworks, and prompt engineering.
Experience using distributed compute platforms such as Apache Spark, Ray, or Dask to process large-scale text corpora.
Hands-on experience implementing vector indexing and similarity search engines such as Milvus, Qdrant, Pinecone, or OpenSearch.
Familiarity with deploying and monitoring AI/ML workloads across AWS, GCP, or Azure.
Experience implementing model governance, data security, and mitigation techniques for model bias, hallucinations, and adversarial prompt attacks.

Responsibilities

Lead the design, development, and deployment of advanced machine learning models and computational linguistics pipelines.
Transform large unstructured text and multimodal datasets into structured intelligence.
Develop production-grade inference pipelines.
Collaborate with cross-functional engineering teams to integrate advanced models into enterprise operational environments.
Build and fine-tune neural network architectures using Python and modern ML/NLP frameworks.
Deploy models into scalable production pipelines using containerization and model-serving frameworks.
Implement quantitative model evaluation metrics and debug complex model edge cases.