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AI Solutions Engineer
Req Id: 1866
State/Province: Arizona
Business: Gore Medical Products
Category: Engineering, Scientists \& Technicians
Sub\-Category: Engineering
Work Arrangement: Hybrid, On\-site
Posting Start Date: 7/15/26
Posting End Date: 7/30/26
About the Role
We are looking for an AI Solutions Engineer to join our R\&D organization. In this role, you will design and implement AI\-enabled tools and workflows that improve efficiency, quality, and decision\-making across regulated medical device processes. You will work cross\-functionally with engineering, quality, regulatory, and process owners to translate complex requirements into scalable digital solutions. This role offers a unique opportunity to shape how AI is applied within R\&D and regulatory environments while delivering high\-impact, enterprise\-level capabilities.
This role will be located at one of our facilities in Phoenix, Arizona with the possibility of a hybrid work arrangement.
Responsibilities
Design and develop AI\-enabled tools that automate and improve complex R\&D and regulatory workflows
Translate regulatory, quality, and engineering requirements into structured templates, logic, and system workflows
Build and maintain applications leveraging platforms such as Power Apps, Power Automate, and related AI capabilities
Drive and partner with IT associates to develop end\-to\-end solutions including architecture, prompt logic, data mapping, and workflow automation
Collaborate with cross\-functional stakeholders to identify opportunities, define requirements, and deliver scalable solutions
Improve user experience and reliability through iterative enhancement of tools, validation checks, and error handling
Drive adoption of AI\-enabled processes by ensuring solutions are intuitive, efficient, and aligned to user needs
Manage multiple initiatives from problem definition through delivery, evaluation, and iteration
Document solutions and ensure compliance with quality system and regulatory requirements
Develop reusable frameworks, patterns, and standards to enable broader scaling of AI solutions
Required Qualifications
Bachelor’s degree in Engineering, Computer Science, or related technical field (or equivalent experience)
Experience in New Product Development or Commercial product engineering support
Experience developing workflow\-based applications or automation solutions
Ability to translate complex technical or regulatory requirements into structured solutions
Demonstrated ability to work across cross\-functional teams and influence without authority
Strong problem\-solving skills and ability to operate in ambiguous environments
Experience working within regulated or quality\-driven environments
Ability to travel up to 10%
Desired Qualifications
Experience with AI\-assisted workflows, LLM\-based tools, or prompt\-driven solution design
Experience with Microsoft Power Platform (Power Apps, Power Automate, Dataverse)
Experience improving process efficiency through automation or decision\-support tools
What We Offer
Our success is based on the capability and creativity of our Associates, and we are proud to offer a comprehensive and competitive total rewards program that supports your everyday and helps you build your tomorrow.
We provide benefits that offer choice and flexibility and promote overall well\-being. And in keeping with our belief that every Associate should share in the collective success of the enterprise; we provide a distinctive Associate Stock Ownership Plan in each country as well as potential opportunities for “profit\-sharing”. Learn more at gore.com/careers/benefits
Gore is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, national origin, ancestry, age, status as a qualified individual with a disability, genetic information, pregnancy status, medical condition, marital status, sexual orientation, status as a protected veteran, gender identity and expression, and any other characteristic protected by applicable laws and regulations.
Gore is committed to a drug\-free workplace. All employment is contingent upon successful completion of drug and background screening. Gore will consider qualified applicants with criminal histories, e.g., arrest and conviction records, in a manner consistent with the requirements of applicable laws.
Gore requires all applicants to be eligible to work within the United States. Gore generally will not sponsor visas unless otherwise noted on the position description.
If you have questions about this opportunity, please contact the Talent Acquisition Specialist, Amy Davis, or People Leader, Kevin Coffman.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At W. L. Gore & Associates, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
W. L. Gore & Associates AI Hiring
W. L. Gore & Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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