At Lyntris, we are building NLP capabilities that translate dense engineering documentation into structured, machine-actionable knowledge. This remote contract role centers on designing and delivering NLP pipelines and LLM-driven document understanding to extract meaningful information from manuals, specifications, and technical reports. The ideal candidate brings hands-on NLP experience and a technical degree, ready to work asynchronously with a cross-functional team.
Responsibilities
- Create and sustain NLP pipelines that tackle technical and structured documents, enabling information extraction, summarization, semantic search, and question answering.
- Develop and fine-tune transformer-based LLM applications, leveraging prompt design and retrieval-augmented generation (RAG) architectures.
- Process and analyze large technical corpora such as manuals, specifications, reports, drawings, tables, and figures.
- Craft methods to convert unstructured and semi-structured documents into structured knowledge for automated downstream use.
- Implement scalable ML solutions in Python using PyTorch and Hugging Face.
- Evaluate model performance, improve accuracy, and optimize inference pipelines for production environments.
- Collaborate with software engineers, data scientists, and domain experts to define requirements and deliver AI-enabled document intelligence solutions.
- Other duties as assigned.
Requirements
- Bachelor's degree in Computer Science, Data Science, AI/ML, or a related technical field, or equivalent hands-on experience.
- 2 to 4 years building NLP pipelines for technical or structured document understanding, including extraction, summarization, semantic search, and Q&A.
- Hands-on experience with large language models and transformer architectures (eg, BERT and successors), including fine-tuning, prompt engineering, pipeline orchestration, and retrieval-augmented generation (RAG).
- Experience processing engineering manuals, specifications, technical artifacts, tables, and figures.
- Strong Python skills and experience with PyTorch and Hugging Face Transformers.
- Proven ability to transform unstructured text into structured, machine-actionable knowledge.
- Must hold an active U.S. national security clearance or be able to obtain and maintain one.
Technologies
- Python
- PyTorch
- Hugging Face Transformers
- BERT
Benefits
- Paid Time Off
- Paid Company Holidays
- Medical, Dental & Vision Insurance
- Optional HSA and FSA
- Base and Voluntary Life Insurance
- Short Term & Long Term Disability Insurance
- 401k Matching
- Employee Assistance Program
Physical Requirements
- Prolonged periods sitting at a desk and working on a computer.
- Must be able to lift up to 15 pounds at times.
Clearance Requirements
Some roles require access to U.S. National Security information and government personnel security clearance (PCL). To qualify, candidates must be U.S. citizens and either currently hold this eligibility or be able to complete the investigation process and maintain eligibility throughout employment.
EEOC & KNOW YOUR RIGHTS
Lyntris companies are Equal Opportunity Employers. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, status as a protected veteran or any other status protected by applicable federal, state, and local law. We ensure that all employment decisions, including hiring, promotion, compensation, and other terms and conditions of employment, are based on merit, qualifications, and business needs.
Preferred Qualifications (Not Required)
- Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.
- Experience deploying and maintaining production-scale NLP or LLM applications.
- Familiarity with vector databases, embedding models, and semantic retrieval systems.
- Experience with document parsing, OCR, layout-aware models, or multimodal document understanding.
- Experience working with engineering, manufacturing, aerospace, defense, or other highly technical datasets.
- Knowledge of MLOps practices, model monitoring, CI/CD pipelines, and cloud-based AI infrastructure.
- Active-duty military experience.
