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About This Role
Position Overview
MEG is seeking a developer with strong programming and AI integration skills to improve engineering workflows and support the company’s long-term technology needs.
This position begins in the engineering department with required, hands-on training in modeling and drafting. The employee will learn our products, design standards, and engineering processes before progressively taking on application development responsibilities. Initial development priorities include custom Autodesk Inventor tools, AI augmentation of 3D design, and an AI-assisted drawing review system integrated with Wrike.
After completing the engineering assignment, the employee will transition to the IT department, supporting company-wide AI development, general IT operations, and continued maintenance of engineering applications.
Required Drafting Training and Progression
The initial drafting assignment serves as a hands-on research and learning period for the development work that follows. By modeling and drafting MEG’s products, the employee will gain firsthand understanding of design requirements, manufacturing considerations, review expectations, and the daily challenges the custom tools must address. This background is necessary to develop useful automation and evaluate whether AI-assisted reviews are identifying relevant issues accurately.
Candidates must be willing to spend an unspecified period performing modeling and drafting work before programming becomes their primary responsibility. Progression will depend on demonstrated drafting proficiency rather than a fixed timeline.
The drafting department will provide training through its established program. The employee will model and draft a wide variety of products and develop proficiency in Autodesk Inventor, Autodesk Vault, and MEG’s documentation practices.
Progress will be measured against the qualification matrix used within MEG’s ISO quality management system. The Drafting Manager will determine proficiency with input from team leads.
As proficiency increases, the employee will begin applying that knowledge to development projects, with a growing portion of the workday allocated to programming. For example, an initial allocation might include seven hours of drafting and one hour of programming, progressing toward full-time development as qualifications and project needs allow. These allocations are illustrative and are not a guaranteed schedule.
The employee will report to the Drafting Manager until drafting training is complete, at which point the employee will then report directly to the Director of Engineering until engineering assignments are complete and the employee is ready to transfer to IT.
Engineering Responsibilities
- Complete modeling and drafting assignments while learning MEG’s products, manufacturing requirements, design standards, and review processes.
- Work with engineering personnel to translate identified needs into practical software requirements and measurable goals.
- Independently research and select development methods, tools, and approaches appropriate to each project.
- Develop, test, document, and implement custom Inventor tools, add-ins, and workflow automation using Autodesk-supported APIs and development methods.
- Investigate and implement AI augmentation of 3D modeling and design through Autodesk-supported capabilities and integration methods.
- Create, validate, and implement an AI-assisted drawing review system integrated with Wrike, drawing on more than ten years of historical drawing reviews.
- Develop review capabilities addressing drawing layout, technical data, bills of materials, typographical errors, and other recurring model and drawing review findings.
- Evaluate the feasibility of comparing models and drawings against piping and instrumentation diagrams (P&IDs), and coordinating review tools with supported Inventor AI capabilities.
- Collaboratively evaluate results with the engineering team, measure missed issues and incorrect findings, and refine applications based on demonstrated performance.
- Prepare user instructions, technical documentation, and training materials; support implementation and ongoing improvements.
Engineering will provide project inputs, priorities, and desired outcomes. The developer will establish implementation approaches and monitor results collaboratively with the team. IT will establish approved platforms and security requirements, including protection of confidential and NDA-covered customer information.
Responsibilities Following Transition to IT
Within MEG’s approximately 300-person organization, responsibilities will include:
- Develop and maintain internal AI tools and integrations for departments throughout the company.
- Continue supporting engineering applications, Inventor tools, and the Wrike AI review system.
- Respond to IT support tickets and troubleshoot hardware, software, access, and application issues.
- Assist with user accounts, permissions, workstation setup, software deployment, and routine systems administration.
- Support network, server, backup, and other IT infrastructure activities as assigned.
- Assist with website and web application security, updates, access controls, and vulnerability remediation.
- Maintain technical documentation and help employees use company technology effectively.
Required Qualifications
- Strong practical experience developing AI-enabled applications and integrating AI capabilities into business workflows.
- Demonstrated programming ability, including the ability to independently develop, troubleshoot, test, and maintain applications.
- Experience connecting applications and data through APIs or other supported integration methods.
- Ability to translate business needs into workable technical solutions and explain technical limitations clearly.
- Willingness to complete the required drafting assignment and progress according to demonstrated proficiency.
- Careful attention to detail and a disciplined approach to evaluating software and AI outputs.
- Ability to work independently while collaborating closely with technical and nontechnical personnel.
- Commitment to protecting confidential information and following company security requirements.
- Ability to work onsite and travel less than 10%.
Beneficial Qualifications
- A degree in computer science, computer engineering, or a related discipline. A degree is preferred but not required; relevant experience and demonstrated ability will be considered.
- Experience with one or more applicable languages, including C#, VB.NET, VBA, Python, or C++.
- Familiarity with Autodesk Inventor, Vault, iLogic, Autodesk APIs, or add-in development.
- Experience with AI analysis of documents, images, or technical drawings; retrieval of historical information; and evaluation of AI accuracy.
- Experience with Wrike, workflow automation, or project management system integrations.
- Familiarity with manufacturing drawings, assemblies, bills of materials, or P&IDs.
- Experience with source control, software deployment, technical documentation, and user training.
- Familiarity with Windows systems administration, networking, web security, and general IT support.
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 Martin Energy Group Services LLC, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Martin Energy Group Services LLC AI Hiring
Martin Energy Group Services LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tipton, MO, US.
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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