Interested in this AI/ML Engineer role at Gates Corporation?
Apply Now →About This Role
Date: Oct 1, 2026
Location: Englewood, CO, US, 80112
Company: Gates Corporation
Are you inspired by challenging the status quo? Do you thrive in collaborative environments that drive results? If so, Gates could be for you.
Gates is a leading manufacturer of application-specific fluid power and power transmission solutions. We push the boundaries of material science to engineer solutions that continually exceed customer expectations.
Let's simplify it, think belts and hoses. Found in motorcycles, conveyor belts, cars, tractors, blenders, vacuum cleaners, bicycles, & 3D printers just to name a few. Because why not do it all?
Essential Duties and Responsibilities
Gates Corporation is seeking Rotational AI Engineers for the 2027 class to accelerate innovation across our global product portfolio by applying artificial intelligence, machine learning, and advanced analytics to engineering and product development processes.
This rotational engineer will work at the intersection of engineering, data, and digital technology, partnering with product, process, and materials development teams to optimize design efficiency, predictive performance modeling, and testing automation for Gates’ power transmission and fluid power products.
The rotational assignments will take place through the different functional contributors of the Gates Eco-InnovationTM system.
- Design, develop, and deploy AI/ML models to support product design, simulation, testing, and validation activities.
- Apply machine learning techniques to predict product performance, durability, and failure modes using historical test, field, and simulation data.
- Develop AI-enabled tools for design optimization, including automated parameter tuning, materials selection, and geometry optimization.
Keys to Success
- Internship or Co-Op experience in applying machine learning or advanced analytics.
- Excellent communication (verbal and written), ability to work as a contributing member of a fast-paced engineering team
- Positive attitude and strong desire to learn
- Prior experience using AI/ML software
- Understanding of engineering systems and physical products
- Exhibits the Gates values in all actions and decisions (Values can be found at https://www.gates.com/us/en/about-us/company-overview.html)
- Global mindset and the ability to thrive in a diverse culture and environment
Supervisory Responsibilities
- None
- No supervisory responsibilities; accountable for own contribution
Requirements and Preferred Skills
- Must be a current B.S. or M.S. in Data Science, Math, or Computer Science with an expected Graduation date between December 2026 and June 2027
- Must be legally authorized to work in the United States without sponsorship
Pay & Benefits
- Full-Time
- Salary range: $72,500-$ 77,500
- Sign-On Eligible
- Relocation is provided
- Medical, Dental, Vision insurance and other voluntary benefit options: benefits begin on the first day of the month immediately following your date of hire
- Eligible for 3 weeks of paid vacation + 11 holidays (9 scheduled & 2 floating) + 8 sick days. All vacation days are accrued
- 401(k): 3% company contribution and additional 3% company match
- Tuition Reimbursement
Why Gates?
Founded in 1911 in Denver, Colorado, Gates is publicly traded on the NYSE. While we might operate in a vast amount of time zones we operate as 'One Gates' and have a common goal of pushing the boundaries of materials science. We invest in our people, bringing real-world experience that enables us to solve our customers' diverse challenges of today and anticipate those of tomorrow.
Work Environment
Gates is an Equal Opportunity and is committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job-related reasons regardless of race, sex, color, religion, age, disability, pregnancy, citizenship, sexual orientation, gender identity, national origin, protected veteran status, genetic information, marital status, or any other consideration defined by law.
While performing the duties of this job, the employee is frequently required to sit; use hands and fingers to work with objects, tools, or controls; and use office equipment including computers, telephones, and/or copiers/scanners. The employee must frequently lift and/or move up to 10 pounds.
For individuals assigned and/or hired to work in Colorado, Gates is required by law to include a reasonable estimate of the compensation for this role. This compensation range is specific to the State of Colorado and takes into account various factors that are considered in making compensation decisions, including but not limited to the candidate's relevant experience, qualifications, skills, competencies, and proficiency for the role.
#LI-BD1
Nearest Major Market: Denver
Salary Context
This $72K-$77K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Gates Corporation, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($75K) sits 65% below the category median. Disclosed range: $72K to $77K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Gates Corporation AI Hiring
Gates Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Englewood, CO, US. Compensation range: $77K - $77K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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