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About This Role
Matrix designs, manufactures, and sells innovative technological products that help keep people safe. Originally focused on the underground coal mining industry, Matrix has expanded into new industrial markets in the United States and globally.
We are currently seeking a Senior Computer Vision Engineer. The Senior Computer Vision Engineer will work closely with software engineers, as well as project and product managers, to develop robust vision\-based artificial intelligence algorithms. In this role, the engineer's enthusiasm and expertise in computer vision engineering will be directed toward designing safe and effective computer vision solutions. This position is based in a collaborative, team\-oriented environment at the company's main office in Newburgh, IN, and is not open to remote work arrangements. This position works in a team environment at our office in Newburgh, IN. This is not a remote position.
This position reports to the Manager of Software Systems.
Duties and Responsibilities
- Prepare customer\-facing presentations for custom model deployments
- Implement CI/CD pipelines for models, data, and source code
- Work with product and project managers to ensure that projects proceed on time and on budget
- Work with other engineers to develop a working understanding of how the AI is developing
- Document process steps to ensure reasonable human oversight
- Work with other engineers to monitor changes in development and implement transfer learning and knowledge distillation between iterative machine learning models
- Mentor junior engineers and interns
- Participate in code reviews and sprint planning
- Understand and apply best practices for object detection modeling
- Understand and apply TensorFlow, PyTorch, and ONNX core concepts
- Understand and apply hardware accelerator compilation and execution
- Understand and apply Jupyter Notebooks/Google Colab concepts
- Understand and apply source control best practices for machine learning, ETL, and data annotation pipelines
Qualifications \& Competencies
Employment Eligibility \& Verification
All applicants must be able to provide proof of eligibility to work in the United States. Employment is contingent upon the successful completion of the I\-9 form, as required by federal law. Additionally, candidates will be required to undergo an employment verification process before beginning work. Please note that we do not offer sponsorship for work authorization (e.g., H\-1B, TN, or other visas) at this time.
Education: Bachelor's degree in a related field, such as computer science, software engineering or data science, is recommended. Master's degree in Artificial Intelligence a plus.
- 7\+ years of experience in the ML space
- An expert\-level understanding of Python with a focus on ML framework such as TensorFlow or PyTorch
- Proficient with state\-of\-the\-art object detection algorithms such as YOLO, DETR, and DINO
- Experience with containerization technologies such as Docker, Kubernetes, etc.
- Experience developing software using one or more of the following languages (C/C\+\+, C\#, Python)
- Experience with Linux and/or Windows OS
- Experience with model, container, and package registries
- Experience with ML tools such as DVC, MLFlow, Lake FS, Label Studio, Azure ML, Azure AI Foundry
- Experience with SQL databases, vector embeddings, and vector databases such as Milvus, pgvector, Pinecone, Chroma, etc.
- Experience with multimodal models such as CLIP, GPT\-4V, Llama
- Experience with classical CV algorithms such as Canny Edge Detector, SIFT, RANSAC, Optical Flow, SLAM, etc.
- Strong understanding of source control concepts and CI/CD pipelines
- Must have strong communication, computer, documentation, presentation, and interpersonal skills
Working Conditions
- Daily Job duties will consist of office, lab, and desk work with occasional field testing required.
- Candidate may be required to work underground, but on an infrequent basis.
- While performing the duties of this job, the employee is regularly exposed to work near large moving machinery.
- Customer operations may contain airborne particles and allergens.
- Matrix employees are furnished and required to wear safety gear such as hard hats, steel toe shoes, reflective clothing, earplugs, and safety glasses.
- Occasional travel and ability to work various shifts as required by customer.
Physical Requirements
The employee is occasionally required to stand, walk and stoop, or crouch. The employee may need to lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception and ability to adjust focus. Physical Demands: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Benefits
- First day coverage of all benefits \- no waiting period
- Premium free medical, dental and vision insurance – working spouse must take single major medical at their place of employment if offered
- On\-site health clinic
- Basic Life (2x annual base salary at no cost)
- Optional Life and Accidental Death and Dismemberment (AD\&D) insurance
- Short\-Term and Long\-Term Disability insurance (no cost)
- 401(k) Plan with up to an 8% company match
- FSA for Health Care and Dependent Care
- 10 Paid annual holidays plus vacation time
- Educational Reimbursement Program
- Scholarship Program
- Optional Gym Membership
- ESports Room
\#LI\-Onsite
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 Matrix Design Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
Matrix Design Group, LLC AI Hiring
Matrix Design Group, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newburgh, IN, 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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