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About This Role
Machine Learning Engineer
About this Role
Due to the nature of our work, US Citizenship is required with an ability to obtain secret clearance.
Are you passionate about machine learning and eager to work on cutting\-edge aerospace projects? Skyward, Ltd., a pioneering aerospace company based in Dayton, is seeking a talented Machine Learning Engineer (not entry level) to join our Modeling \& Simulation team. This is a growth\-oriented opportunity, perfect for a self\-driven individual who is looking to expand their skill set and work into a leadership role. Supervised, unsupervised and reinforcement learning and computer vision experience are all valuable areas of interest.
In this role, you'll be exposed to the development and implementation of cutting\-edge machine learning algorithms to tackle fascinating challenges like modeling temporal sensor data, analyzing video data from experiments or unmanned aerial system (UAS) data collection, and ensuring data integrity. You will assist Skyward in the development of potential products related to additive manufacturing, structural analysis, or flight navigation, for example.
Not only will you get to work with state\-of\-the\-art technology, including accelerated hardware and supercomputing resources, but you'll also be instrumental in crafting optimized machine learning solutions for data\-intensive applications.
Key Responsibilities:
- Develop ML Software: Build end\-to\-end machine learning solutions from data pre\-processing to inference.
- Create Data Pipelines: Develop high\-performance pipelines to clean, augment, and curate datasets for machine learning.
- Customize Models: Modify popular pre\-trained Machine Learning models to meet specific project needs.
- Model Development: Design and implement machine learning models for a variety of data types including imagery, time\-series, and numeric data. Identify appropriate machine learning and computer vision techniques to successfully model task objectives for a variety of data types.
- Analyze and Evaluate: Assess the efficiency and effectiveness of machine learning solutions and integrate them with existing software.Organize and analyze data to derive conclusions, insights, and confidence level.
- Collaborate: Work closely with the Skyward team and participate in a unique cross\-domain opportunity to integrate machine learning with state\-of\-the\-art dynamic simulations and testing.
- Drive Solutions: Meet with customers and partners to better understand their challenges and work with subject matter experts to develop tailored solutions. Write reports, develop presentations, and take an active role in the development of new proposals.
- Support Testing: Support ballistic test data collection and analysis and develop algorithms to improve processes.
Skills and Proficiencies:
· End\-to\-End ML Software: Proven experience in developing machine learning software.
- Technical Expertise: Proficiency in common programming languages/packages related to machine earning such as Python, C\+\+, TensorFlow/Pytorch, Numpy, Pandas, MatPlotLib, etc.
- Data Processing: Strong skills in transforming and processing data, developing data pipelines, and understanding issues with data integrity.
- Communication: Excellent communication skills to foster cooperative, working relationships.
- Self\-Starter: Motivated, proactive, and willing to learn.
Minimum Qualifications:
- Citizenship: US Citizenship Required with an Ability to Obtain Secret Clearance
- Education: Degree in Computer Science, Software Engineering, or related field with equivalent work experience. Master’s Degree, or Bachelor’s degree with 3 years work experience, is desired.
About Skyward, Ltd.
Founded in 1997, Skyward, Ltd. began with a vision to provide customers with more personal and detail\-oriented professional services. Today, Skyward has expanded its capabilities to provide custom product solutions to customer problems utilizing technology, research and development. Skyward’s expertise includes test \& evaluation, modeling \& simulation, and game\-changing areas such as unmanned aerial systems (UAS), additive manufacturing, and machine learning.
Skyward works on a diverse range of projects, from survivability assessments to fire protection technology development. With access to advanced tools and resources, including modeling and simulation techniques and machine learning, you'll play a crucial role in delivering high\-quality solutions to our clients.
At Skyward, we pride ourselves on our commitment to making innovative solutions for our customers. And with a focus on collaboration and continuous improvement, you'll have the opportunity to grow and develop your skills alongside experienced professionals in the field.
If you're looking for a rewarding career in engineering, Skyward, Ltd. could be the perfect fit for you. Join us and be part of a team that values professionalism, integrity, and excellence.
Benefits:
Skyward offers competitive salaries and benefits, including:
· 401K retirement plan with annual employer contributions
· Comprehensive health insurance package, including dental and vision
· Employer\-provided Health Savings Account contributions
· Life insurance
· Paid Federal holidays
· Remote working opportunities
· A generous paid time off plan
Skyward Ltd. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, marital status, veteran's status, disability, sexual orientation, gender identity, or any other protected class set forth by federal or state law.
Skyward is a Drug\-Free Workplace
All benefits are subject to plan eligibility rules, and legal requirements; from time\-to\-time benefits may be updated or modified.
This job description is designed to provide general guidance in job tasks and is not meant to be all\-inclusive of the responsibilities, duties, and skills required of this position. As business demands and needs change, the essential functions of this position may be updated to reflect the needs of Skyward, Ltd.
Pay: From $100,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Work Location: In person
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Employee Management Services, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Employee Management Services AI Hiring
Employee Management Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dayton, OH, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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