Machine Learning Engineer

A remote contract role at Lyntris to build NLP pipelines and LLM powered document understanding to transform engineering documentation into structured, machine-actionable knowledge.

Responsibilities

• Design, implement, and maintain NLP pipelines for technical and structured document understanding, including information extraction, summarization, semantic search, and question answering.
• Build and optimize LLM powered applications using transformer-based models, including fine-tuning, prompt engineering, and retrieval augmented generation (RAG) architectures.
• Process and analyze complex technical corpora such as engineering manuals, specifications, technical reports, drawings, tables, and figures.
• Develop methods to convert unstructured and semi-structured documents into structured, machine-actionable knowledge for downstream applications.
• Implement scalable machine learning solutions using Python and modern frameworks such as PyTorch and Hugging Face.
• Evaluate model performance, improve accuracy, and optimize inference pipelines for production environments.
• Collaborate with cross-functional teams including software engineers, data scientists, and subject matter experts to define requirements and deliver AI-enabled document intelligence solutions.
• Performs other duties as assigned.

Requirements

• Bachelor's degree in Computer Science, Data Science, AI/Machine Learning, or a related technical field (or equivalent practical experience).
• 2-4 years of experience building NLP pipelines for technical or structured document understanding, including extraction, summarization, semantic search, and question answering.
• Hands-on experience with large language models and transformer architectures (BERT and successors), including fine-tuning, prompt engineering, pipeline orchestration, and retrieval augmented generation (RAG).
• Experience processing complex technical documentation such as engineering manuals, specifications, technical artifacts, tables, and figures.
• Strong proficiency in Python and modern ML frameworks, including PyTorch and Hugging Face Transformers.
• Demonstrated experience converting unstructured text into structured, machine-actionable knowledge.
• Currently holds an active U.S. national security clearance or be able to receive 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 positions will require access to U.S. National Security information. Positions that require this access will be required to receive and maintain a U.S. government personnel security clearance (PCL). In order to qualify for this position, the candidate must be a US citizen and either currently possess this National Security eligibility or be able to complete the investigation application process with a favorable determination and maintain that eligibility throughout their 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.