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About This Role
Description:
About the Company:
Etegent Technologies is a defense\-focused technology company with offices in Beavercreek and Blue Ash, OH. We are a multi\-disciplinary team of engineers, scientists, and management professionals dedicated to developing and commercializing AI/ML/Software solutions that address our customers’ most significant challenges and opportunities.
Most important to us are our culture and values. Etegent’s culture embodies respect, authenticity, collaboration, flexibility, curiosity, and fun.
The Position:
Etegent is seeking Machine Learning Engineers (MLEs) to work with our Intelligence, Surveillance, and Reconnaissance (ISR) group based in the Beavercreek office. MLEs will work in a team environment with Etegent teammates, customers, and other researchers from leading academic institutions, government organizations and commercial entities.
We deliver solutions in the domains of AI/ML, data science, software, and visualization to address geospatial intelligence (GEOINT) challenges for Department of Defense and Intelligence Community (DoD/IC) customers. We develop data science tools to analyze large, diverse datasets from a variety of sensor types (SAR, ISAR, GMTI, HIS, EO, IR). We design and implement algorithms to perform pattern recognition, such as statistical and deep learning techniques, to identify meaningful trends and patterns. We wrap these algorithms in accessible, scalable software to be deployed on customer systems, often requiring containerized software and custom user interfaces for visualization.
This position is based in Beavercreek, OH with the expectation of being in Etegent’s office or at a customer facility 5 days a week.
The Candidate:
More so than seeking a candidate with specific training and experience, Etegent is seeking a candidate with an innate curiosity and passion for learning and understanding, tempered by a recognition of the pragmatic constraints inherent in developing real solutions to real problems. Etegent needs an MLE who is flexible and adaptable, having the desire and ability to learn new skills and develop new capabilities to take on challenges outside their current realm of experience. We are seeking very sharp, curious, well\-rounded, adaptable people who can learn and grow as required to confront the challenges at hand.
Job Responsibilities:
- Effectively communicate status updates, and other important information, with the team and group leader.
- Execute research\-and\-development tasks in close collaboration with the team and group leader according to a set project schedule.
- Gives effective feedback to others; contributes to the writing of project updates and final reports.
- Understand, design, and implement algorithms for signature exploitation. This includes traditional feature extraction and state\-of\-the\-art deep learning approaches.
- Implement efficient signal/image processing techniques including radar signal processing, electro\-optical and infrared image processing.
- Develop and deploy data science tools for ingestion, visualization, and analysis of large, diverse data sources.
- Implement machine learning, data mining and statistical algorithms for pattern recognition and anomaly detection.
- Provide expertise in data analytics and algorithm development for diverse data sources and applications.
- Apply statistical techniques to evaluate algorithm performance.
- Coordinate with teammates and users to instantiate research concepts in efficient, modular, and well\-documented software.
Requirements:
Required Qualifications:
- Bachelor’s Degree in the fields of Computer Science, Engineering, Physics, Applied Math, or Statistics.
- Previous experience signal processing, computer vision, or machine learning experience.
- Experience developing ML models using standard ML frameworks (PyTorch, TensorFlow, or similar).
- Background in data science, statistics, and/or computer vision.
- Experience with Python, Git, and Linux.
- Ability to acquire and maintain up to a TS security clearance.
Additional Qualifications for Senior\-Level Candidates:
*Senior\-level candidates should also demonstrate:*
- Experience building and maintaining successful relationships with R\&D customers while supporting business growth and development efforts.
- Previous experience leading cross\-functional project teams and driving projects to successful completion.
Preferred Qualifications:
- Active TS security clearance.
- Experience with remote sensing data products such as SAR, ISAR, EO, GMTI, HSI, and OPIR.
- Experience creating python\-based user interfaces for data visualization.
- Experience developing scalable, modular software tools.
- Familiarity with DoD/IC customers.
- Experience maintaining successful relationships and developing business with R\&D customers.
- History of technical presentations and publications in relevant fields.
Attributes:
- Independent and innovative thinker with an ability to work in an autonomous environment.
- Demonstrates curiosity with a focus on innovating solutions for tasks.
- Effective communicator who can collaborate with teammates and customers to solve problems.
- Able to take personal ownership and responsibility over their work.
Working at Etegent:
At Etegent, you'll be a part of a fast\-growing organization that combines a small\-company feel with big\-company resources and opportunities. We know that work\-life balance is incredibly important, which is why in addition to your competitive salary, medical/dental/vision plan, and a generous annual company 401(k) contribution, you'll enjoy the following:
- Flexibility: Not a morning person? No problem. We only ask that you begin your day by 10:00am. Also, you will enjoy a flexible schedule and some ability to telecommute.
- Casual Dress: We know that incredible things can be achieved by people in casual clothing. We allow employees to dress in the way that is the most comfortable to them.
- Professional Development: Continuous learning on us. Reimbursement provided for up to 100% of qualifying education expenses.
- Food: Keep your energy levels up with our well\-supplied snack and beverage kitchen and enjoy a weekly lunch with your talented colleagues on Free Lunch Thursdays.
- Fun employee events: Enjoy frequent employee get\-togethers outside of work!
Competitive compensation based on qualifications and experience of candidate.
Etegent Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sexual orientation, gender identity, national origin, protected veteran status or disability.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Etegent Technologies, LTD, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Etegent Technologies, LTD AI Hiring
Etegent Technologies, LTD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beavercreek, OH, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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