Interested in this AI/ML Engineer role at Kubota Manufacturing of America?
Apply Now →Skills & Technologies
About This Role
Job Summary
We are seeking a driven and innovative Computer Vision & Automation Specialist to join our team in a fast-paced manufacturing environment. This role is ideal for a technology-focused professional who enjoys using automation, computer vision, and data-driven solutions to solve real-world manufacturing challenges and improve safety, quality, and operational efficiency. As a key partner to Engineering and Production teams, you will design, develop, and continuously improve automated manufacturing processes with a strong focus on Computer Vision applications, Python-based solutions, and Manufacturing Digital Transformation (DX) initiatives. You will also apply foundational knowledge of PLC controls to support the effective integration of computer vision and automation technologies with manufacturing equipment and processes.
If you enjoy combining technology, programming, automation, and manufacturing to develop practical solutions, eliminate inefficiencies, and advance smart manufacturing, this is an excellent opportunity to grow your career while making a direct impact on the future of manufacturing operations.
Benefits
- 401(k) plan with company match
- Comprehensive medical, dental, and vision insurance
- Earned paid time off (ETO)
- 10 paid company holidays annually
- Performance-based merit increases
- Annual bonus opportunity
Core Responsibilities
Computer Vision & AI Systems Development
- Lead the development and deployment of computer vision inspection systems for manufacturing and production environments.
- Design, build, and maintain end-to-end machine vision pipelines using Python and modern ML/CV frameworks such as YOLO, Florence-2, SAM, OpenCV, and PyTorch.
- Own the full model lifecycle, including data collection, dataset creation, annotation, model training, validation, and performance optimization.
- Develop custom OCR and defect-detection solutions capable of operating at production-line speed across multi-camera inspection systems.
Manufacturing Automation & System Integration
- Integrate vision systems with plant automation infrastructure, including PLC communications and manufacturing control systems.
- Design and implement error-proofing (poka-yoke) solutions utilizing automation, sensing, and machine vision technologies to prevent defects and improve product quality.
- Support equipment and automation integration by applying knowledge of PLC controls, including Allen-Bradley/Rockwell Automation platforms, tag structures, and ladder logic.
- Collaborate with engineering, operations, and production teams to identify inspection opportunities and implement scalable technical solutions.
Deployment, Reliability & Operational Support
- Deploy and maintain applications within containerized environments using Docker and version control systems such as Git.
- Implement real-time monitoring, alerting, and reporting systems to ensure inspection reliability, uptime, and responsive issue resolution.
- Troubleshoot and continuously improve system accuracy, speed, robustness, and performance in production environments.
- Build and maintain operator-facing dashboards and user interfaces to improve visibility, usability, and decision-making.
Data Analysis, Model Optimization & Continuous Improvement
- Utilize operational and model performance metrics to improve detection accuracy, precision, recall, throughput, and system effectiveness.
- Establish best practices for computer vision deployment, data management, and model maintenance in industrial environments.
- Research and evaluate emerging AI, machine vision, robotics, and digital transformation technologies to identify opportunities for innovation.
Technical Leadership, Documentation & Cross-Functional Support
- Serve as a technical resource for advanced vision, OCR, and AI-driven inspection initiatives.
- Document system architecture, development methods, testing results, and performance metrics to support scalability and knowledge sharing.
- Support team success by sharing technical expertise, assisting engineers and staff, and contributing to cross-functional improvement initiatives.
Quality, Safety & Compliance
- Ensure compliance with company standards, customer requirements, and applicable regulations while promoting safety, quality, and operational excellence.
- Support continuous improvement activities that strengthen manufacturing quality and process performance.
Minimum Qualifications
- Strong technical problem-solving and analytical skills
- Proficiency in Python programming
- Experience developing software, automation, computer vision, robotics, machine learning solutions or other related STEM activities
- Knowledge of computer vision and image processing concepts
- Strong communication, collaboration, and project execution skills
- Demonstrated ability to troubleshoot and solve complex technical problems
Preferred Qualifications
- Experience in a manufacturing environment, preferably in automation or advanced manufacturing
- Strong experience with Computer Vision, AI-based vision solutions, and image processing applications
- Proficiency in Python programming for automation, data analysis, vision system development, and manufacturing digital transformation projects
- Experience developing and implementing Manufacturing DX solutions to improve performance, traceability, and decision-making
- Basic understanding of PLC controls, including Allen-Bradley/Rockwell Automation, tag structures, and ladder logic
- Familiarity with industrial connectivity tools such as Kepware
- Experience with software development tools such as Visual Studio Code, Github Copilot, and GitLab
- Knowledge of Neural Networks and machine learning concepts related to industrial vision applicationsExperience with Apriso integration or similar manufacturing software platforms
Education Requirements
Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Robotics, Mechatronics, Automation, or a related technical field. Equivalent combinations of education and demonstrated experience in automation, computer vision, software development, or manufacturing technology will be considered.
In lieu of a degree, we consider equivalent, job-related experience, military service, professional certifications, and demonstrated skills. Equivalency determinations are made based on the essential functions of the role.
Language Requirements
Must be able to read, write, understand, and communicate effectively in English.
Work Environment
The physical demands and work environmental characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of the job. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.
Physical Demands
While performing the duties of this job, the team member must have the ability to work in extreme hot or cold conditions, stand for the duration of the shift, uses hands to finger, handle or feel objects, tools or controls; reach with hands and arms; ascend stairs; balance; stoop, kneel or crouch; talk and hear. In addition, the team member must occasionally lift up to (“20”, “50”, etc.) lbs. Specific vision abilities required by the job include close vision, distance vision, peripheral vision, depth perception and the ability to focus.
DISCLAIMER
The information provided in the description has been designed to indicate the general nature and level of work performed by incumbents within the classification. This description is not intended to be a comprehensive inventory of all duties, responsibilities, qualifications and working conditions required of employees assigned to this job/classification. This job is intended to include the current essential functions of the job. Management reserves the right to add or modify the duties and responsibilities and to designate other functions as essential at any time.
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 Kubota Manufacturing of America, 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.
Kubota Manufacturing of America AI Hiring
Kubota Manufacturing of America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Gainesville, GA, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.