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About This Role
RFA Engineering (www.rfamec.com (https://www.rfamec.com)) supports industry-leading clients through the full software development lifecycle to build cutting-edge precision agriculture, machine guidance, vehicle automation and autonomy applications. We are seeking passionate, talented engineers to work on exciting projects using the latest tools and technologies including robotics, computer-vision, machine learning, IoT, cloud computing, and much more. Collaborate with a team of industry experts onsite at our client's world-class engineering center and contribute to developing innovative solutions that drive sustainable agriculture practices.
This is a full-time position with a full benefit package listed below that includes opportunities for professional growth, direct hire by our customers, and additional opportunities within our own organization.
Software Engineer – Computer Vision & Machine Learning
RFA Engineering is seeking a Software Engineer in the field of Robotics, Perception, Vision Processing and Machine Learning to architect, develop and integrate new intelligent features into the next generation of agricultural and off-highway equipment.
Responsibilities
- Develop machine learning and computer vision algorithms for automation and autonomy product development.
- Design, implement, and optimize machine learning algorithms and computer vision solutions for production intent hardware. Work with Stereo, monocular cameras, Lidar, radar, edge compute devices.
- Data collection and processing for CVML algorithms development. Utilize data-driven insights to inform your development process and improve models/algorithms.
- Quickly learn new technologies, tools, and methodologies in the fields of computer vision and machine learning. Stay abreast of the latest research and trends, applying this knowledge to your work and sharing insights with the team.
- Primary needs relating to theoretical geometric computer vision - involving concepts like singletons, SLAM (localization, mapping), triangulation, projective geometry, feature detection, depth estimation, camera models/calibration/rectification
- Secondary needs relating to Machine Learning algorithm development. All aspects of model architecture development including: problem definition, data collection/prep, model selection, architecture design, training , evaluation, optimization, deployment, monitoring/maintenance.
Technical Qualifications:
- Experience with data visualization, data management, data querying (eg SQL, Tableau, PowerBI, matplotlib)
- Experience developing and understanding computer vision and machine learning algorithms (models development in pytorch, tensorflow, experience with linear algebra, optimization etc.)
- Ability to quickly learn state of the art technologies, state of the art research in the domains of computer vision, AI, robotics and be able to implement.
- Competent in generating, interpreting, and communicating statistical data well and measuring the right statistics to answer the right questions
- Comfortability reading documentation to read and write good code
- Proficiency in C++ and/or Python.
- Experience with Databricks, AWS, cloud
- Technical expertise matters less in comparison to intelligence, work ethic, and willingness to learn.
Personal Qualification
- Exceptional work ethic and willingness to learn.
- Trailblazing spirit: Eager to solve problems in the autonomous robotics and AI space that have never been solved before.
- Passionate about artificial intelligence, machine learning, robotics, autonomy, and automation.
- Eager to OWN work fully and to make decisions with autonomy to deliver.
Pay Range: $90,000-$170,000 – Commensurate with experience
Visa Sponsorship is NOT available for this position.
About RFA Engineering
RFA Engineering has provided product development and engineering services to industry leading customers since 1943. Our primary focus is the development of off highway equipment including agricultural, construction, mining, recreational, industrial, and special machines. Our work includes concept development, product design, documentation, problem-solving, simulation, optimization, and testing of components, systems and complete machines. Our engineering staff is located at our Engineering Center in Minneapolis, branch office in Dubuque, IA, and at numerous customer sites throughout the U.S.
Competitive Benefits
- Health and Dental Insurance
- TelaDoc Healthiest You
- Supplemental Vision Insurance
- Company Paid Life Insurance
- Company Paid Long-Term Disability
- Short-term Disability
- Retirement Savings Account (Traditional 401k & Roth 401k)
- Flexible Spending Plan Dependent Care
- HSA for Medical Expenses
- Bonus Plan (Exempt Employees Only)
- Paid Time Off (PTO)
- Paid Holidays
- Bereavement Leave
- Employee Assistance Programs (EAP)
- Education Assistance
Equal Opportunity and Veteran Friendly
Education
Preferred* Bachelors of Science or better in Electrical Engineering or related field
- Bachelors of Science or better in Software Engineering or related field
- Bachelors of Science or better in Computer Engineering or related field
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $90K-$170K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 RFA Engineering, 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. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $90K to $170K.
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.
RFA Engineering AI Hiring
RFA Engineering has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cary, NC, US. Compensation range: $170K - $170K.
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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