Interested in this AI/ML Engineer role at nou Systems, Inc.?
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About This Role
nou Systems, Inc. (nSI) is a 100% employee-owned company! We value teamwork and collaboration, and this transition allows us to take that commitment to a new level. nSI empowers our customers by supplying products, services, and expertise with powerful, data driven insights that transform defense systems and improve capabilities.
We are pleased to announce the opening for an AI Engineer who will join our Technical Solutions division and become part of our rapidly growing technology company operating in missile defense, cybersecurity, test range modernization, biotechnology, and space control marketspaces. You will be part of a team designing machine learning solutions from the lab to the field.
Responsibilities:
You’ll develop, train, and analyze machine learning models for supervised learning and reinforcement learning. You’ll work on a mix of projects, some of which will include other data scientists, others of which will require you to help educate non-data-science teams on the value and limitations of machine learning solutions. You will:
- Help identify and design training data sets.
- Implement and customize ML models from open literature.
- Develop MLOps pipelines.
- Visualize and communicate your results in written documents and oral presentations.
- You’ll support multiple teams and must be able to work effectively with other teammates.
Basic Qualifications:
- Bachelor’s degree in a STEM field.
- Academic, internship, or professional experience with AI development (RL, XAI, LLM)
- 1-3 years of hands-on experience with deep learning models (supervised, unsupervised, and reinforcement learning) using PyTorch, Tensorflow, or Jax.
- You must possess or can obtain an active Secret clearance.
This is a junior level position. We are looking for hands-on experience with deep learning models as well as software development fundamentals in Python. We are looking for great written and verbal communication skills. We are looking for someone who is a team player and is glad to work on a variety of machine learning projects as opportunity presents.
Lacking any of these qualifications disqualifies you from this job.
Preferred Qualifications:
- Ability to write clean deep learning code in Python.
- Ability and mindset to approach problems with statistical rigor.
- Strong intellectual curiosity. Our solutions often require creative implementations and combinations of state-of-the-art methodology.
- Comfort with self-direction. You'll work with our top technical talent, but we're looking for evidence you can diagnose a problem and approach it independently.
- Experience with time series data, satellite imagery, and graph neural networks.
- Experience with RL development using OpenAI Gymnasium and Ray RLlib.
- Experience with RL algorithms, including Q-Learning and Actor-Critic methods.
- Experience with MLOps tools (PyTorch Lightning, Optuna, MLflow, etc.).
- Experience with DevOps tools (Git, Gitlab, CI/CD, Remote Computing, Docker)
nou (pronounced ‘new’) Systems:
Since its founding in 2012, nou Systems has built a reputation for excellence in innovative engineering, prototype development, and technical and professional services in multiple markets, including missile defense, cybersecurity, test range modernization, and space control. Our diverse and highly skilled team delivers state-of-the-art products from concepts and prototypes to fully developed and integrated solutions.
We offer significant advancement and personal career development opportunities within our dynamic high-tech company. Our culture is firmly established in treating our employees like family.
Benefits of working at nSI
nou Systems, Inc. offers a comprehensive, total rewards package that includes competitive compensation and diverse benefits that reflect our company culture of service, excellence, and a supportive work environment. Benefits may vary based on status but the majority of our positions include the following:
- Competitive Wages*
- Medical, Rx, Dental & Vision Insurance
- Medical plan with Health Savings Account eligibility
- Company-funded Life, Short-Term & Long-Term Disability Insurance
- Personal Time Off, Vacation, and Paid Holidays
- 401(k) Retirement Plan
- Employee Stock Ownership Plan (ESOP)
- Flexible Spending Accounts
- Norton LifeLock
- Tuition Reimbursement
- Personal Development, Technical Learning Opportunities, & Lunch-n-Learns
- Corporate Sponsored Events & Community Outreach
- Final compensation for this position is determined by a variety of factors, such as a candidate’s relevant work experience, skills, certifications, and geographic location.
nSI is an Equal Opportunity Employer
Employment opportunities at nSI are based upon a candidate’s qualifications and capabilities to perform the essential functions of a particular job and are free from discrimination based on race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, protected veteran status, genetic information, or any other characteristic protected by law. For our complete EEO/AA and Pay Transparency statement, please visit www.nou-systems.com/workingatnou. U.S. citizenship is required for most positions.
Salary Context
This $70K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At nou Systems, Inc., 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $70K to $125K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
nou Systems, Inc. AI Hiring
nou Systems, Inc. has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, US. Compensation range: $125K - $200K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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