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About This Role
*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 state\-of\-the\-art 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.
- Stay current with emerging advancements in NLP, LLMs, and document AI technologies, recommending innovative approaches to improve capabilities.
- Performs other duties as assigned.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field (or equivalent practical experience).
- 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…
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
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lyntris, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Lyntris AI Hiring
Lyntris has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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