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About This Role
Software Engineer, Neural Networks
Capstone Research Corporation is seeking to expand our team. Capstone Research Corporation is headquartered in Huntsville Alabama. We are a customer-oriented engineering services company providing world class research and development support across the Department of Defense (DoD): Army, Navy, Air Force, Marines, and Missile Defense Agency (MDA). Capstone offers 100% Company Provided Medical and Dental Insurance, Matching 401K, and Profit Sharing as part of our top-notch benefits package. Compensation for this position will be determined, but not limited to, factors such as responsibilities of the job, education, experience, knowledge, skills, abilities, alignment with market data, and internal equity.
We have an immediate opening for a Software Engineer to work in our Huntsville, AL office. The successful candidate will join the interdisciplinary Modeling, Simulation, and Analysis Group supporting existing Joint Tactical Integrated Fire Control projects/programs. We are seeking a Software Engineer to support the development of advanced software and algorithms for aerospace applications. The successful candidate will work closely with systems, software, and algorithm engineers to develop, integrate, test, and deploy software supporting complex aerospace and mission systems. Candidates with an aerospace background and experience developing, training, and evaluating neural networks are highly desirable. 3-5 years of software engineering experience is desired, including co-op, intern, academic research and/or other work experience. Salary based on relevant experience and education.
Key Responsibilities
- Work on-site within a collaborative team environment to engineer joint tactical integrated fire control solutions
- Design, develop, test, and maintain software using C++ and Python.
- Develop algorithms and software supporting complex aerospace and mission-system applications.
- Analyze complex datasets and develop software and algorithms to extract meaningful information and support engineering decisions.
- Develop, train, test, and evaluate neural networks and machine-learning algorithms.
- Prototype algorithms in Python and transition validated algorithms into efficient, production-quality C++ implementations.
- Develop unit, integration, and system-level tests to verify software performance and correctness.
- Analyze software and algorithm performance and identify opportunities for optimization.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, Aerospace Engineering, Mathematics, or a related technical discipline.
- 1 - 5 years of professional software development experience.
- Strong proficiency in modern C++ and/or Python.
- Experience writing and maintaining unit test with Google Test or similar framework.
- Ability to operate independently with technical guidance.
- Strong understanding of software engineering principles, data structures, algorithms, and object-oriented programming.
- Excellent problem-solving skills and effective verbal and written communication skills.
- Ability to travel approximately 10% of the time.
- 3 references, including 1 personal and 2 who are familiar with the candidate's prior professional work experience.
- U.S. Citizenship
- Must be able to qualify for a DoD Secret or higher clearance
Preferred Qualifications
- Experience with DoD operations, concepts, and technologies and their supporting sensors, effectors, and networks.
- Experience working with CI/CD systems (GitLab CI preferred).
- Professional experience developing software for aerospace, defense, autonomous systems, control systems, or other complex mission systems.
- Experience developing, training, and evaluating neural networks or machine-learning algorithms.
- Understanding of machine-learning and deep-learning fundamentals.
- Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Strong background in data analysis, statistics, probability, and processing of complex datasets.
- MS Software Engineering
Capstone Research Corporation is an Equal Employment Opportunity Company
All Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or status as a protected veteran.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Capstone Research Corporation, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Capstone Research Corporation AI Hiring
Capstone Research Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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