Machine Learning Engineer

Accelint

United States

On-site

USD 130,000 - 170,000

Full time

14 days+

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Benefits offered by this job

Paid Time Off

Paid Holidays

Medical, Dental & Vision Insurance

Optional HSA and FSA

Life Insurance (base and voluntary)

Short/Long-Term Disability Insurance

401k Matching

Employee Assistance Program

Job summary

Lyntris is seeking an experienced Machine Learning Engineer / NLP Engineer to develop intelligent document understanding solutions. You will build NLP pipelines and LLM-powered apps to transform complex technical documents into structured knowledge, using transformer models, RAG, and advanced processing techniques.

Join a defense tech collaboration spanning hardware, software and mission expertise. This role requires handling engineering manuals, specifications and figures with a focus on

Qualifications

Bachelor’s degree in Computer Science, Data Science, AI/ML, or related field, or equivalent practical experience.
2-4 years of experience building NLP pipelines for technical or structured document understanding.
Hands-on experience with LLMs and transformer architectures, including fine-tuning and RAG.
Experience processing engineering manuals, specifications, and technical documents.
Strong Python and ML frameworks like PyTorch and Hugging Face Transformers.
Active U.S. national security clearance or ability to obtain one.

Responsibilities

Design, develop, and maintain NLP pipelines for technical and structured document understanding, including information extraction and summarization.
Build and optimize LLM-powered applications using transformer models, including fine-tuning and RAG architectures.
Process and analyze complex technical corpora such as manuals, specifications, and drawings.
Convert unstructured documents into structured, machine-actionable knowledge for downstream apps.
Implement scalable ML solutions using Python and modern ML frameworks; evaluate and optimize models.
Collaborate with software engineers, data scientists, and SMEs to deliver AI-enabled document intelligence.
Perform other duties as assigned.

Skills

NLP pipelines

Python

PyTorch

Hugging Face

LLMs

RAG

Document understanding

Education

Bachelor's degree in CS/AI or related field

Master's degree (preferred)

Tools

BERT

Vector databases

OCR

Layout-aware models

Job description

This role is contingent upon contract funding.

Lyntris is a defense technology company that connects sensing to action across the connected battlespace. Lyntris brings together the talents of Accelint and Vitesse teams under one mission, with each contributing deep expertise within their domain. Combining differentiated hardware, software and mission expertise, Lyntris helps customers sense threats, make sense of complex conditions and act with greater speed, precision and confidence in contested environments.

Lyntris supports U.S. and allied defense organizations across every branch — including Navy, Army, Air Force, Space Force and allied partners — spanning strategic, operational and tactical missions and every domain: Space, Air, Land, Sea and Cyber. With more than 200 active defense programs, Lyntris works at every level of the mission, from national command authority to the tactical edge.

Solutions are designed by operators who understand the mission, engineered for the conditions that degrade or defeat standard systems, and built on an open, modular architecture that integrates into existing programs without requiring them to start over. Lyntris moves faster than the traditional defense cycle — with integrated design, build and test capabilities in-house — and delivers systems that sustain and endure long after initial fielding.

We are seeking an experienced Machine Learning Engineer / NLP Engineer to develop intelligent document understanding solutions powered by modern natural language processing (NLP) and large language models (LLMs). In this role, you will build and optimize pipelines that transform complex technical documentation into structured, machine-actionable knowledge. You will work with transformer models, retrieval-augmented generation (RAG), and advanced document processing techniques to enable search, question answering, summarization, and information extraction across engineering and technical content.

Duties & Responsibilities

Design, develop, 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, including 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 ML 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.

Required Qualifications

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 (LLMs) and transformer architectures (BERT and successor models), 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 machine learning 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.

Preferred Qualifications (Not Required)

Master's or Ph.D. 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.

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.

Pay Scales & Benefits

The listed pay scale reflects the broad, minimum to maximum, pay scale for this position for the location for which it has been posted and is not a guarantee of compensation or salary. Other compensation considerations may include, but are not limited to, job responsibilities, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, or other applicable factors.

Benefits include: