Interested in this AI/ML Engineer role at Molex?
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About This Role
Your JobMolex is seeking a dynamic and proactive Field Application Engineer (FAE) to lead, promote, and resolve all technical and engineering related barriers in designing Molex connectors, cables, and solutions for Infotech customers. You will not only drive technical engagements with customers in targeted areas of growth but also uncover new opportunities while developing strong relationships with customer engineering. A key function of this role is to promote and secure new design wins for high\-speed copper products and technology tied to our Datacom \& Specialty Solutions Business Unit.
Our Team
Molex creates connections for life by enabling technologies that transform the future and improve lives. With a presence in more than 40 countries, Molex offers a complete range of connectivity products, services and solutions for the data communications, medical, industrial, automotive and consumer electronics industries. Our Datacom and Specialty Solutions (DSS) team designs an extensive product line serving customers in telecommunications, datacom, hyperscalers, cloud, data center and storage applications.
What You Will Do
- Support customer engineering contacts and the Molex sales team to secure new design wins and a convertible Salesforce pipeline that leads to profitable year\-over\-year growth
- Establish strategic, and differentiated, technical relationships with key engineering and SI contacts with the customer
- Work closely with customer development/process/reliability engineers in resolving any technical issues for design\-in of High Speed I/O, Backplane / BTB / Storage Connectors / Cables as well as other copper solutions
- Provide innovative solutions to customer application opportunities utilizing the broad portfolio of Molex offerings and assist the Account Management team in the proposal of those solutions to customer contacts
- Knowledge sharing and collaboration with other Molex FAE's in the Infotech/Datacom space
- Communication and alignment with the other functional groups internal to Molex including Product Management, Global Sales, Advanced Development, Signal Integrity, etc.
- Make recommendations for improvement of interconnect systems and solutions, resolving customer potential or current issues
- Assist Product Marketing in providing insightful information on competition solution reviews, customer systems and processes
- Develop supporting documents/application notes for customers
- Collaborate and develop demos and customer\-facing presentations on technical concepts
Who You Are (Basic Qualifications)
- Bachelor's Degree in Mechanical Engineering, Electrical/Computer Engineering, Computer Science, Physics or related field
- Minimum 3 years' relevant engineering experience within the electronics industry
- Experience directly interacting with customers
- Experience managing and leading projects, dynamically prioritizing assignments, and working independently
- Experience in one or more of the following areas: Optics, High\-speed Copper Backplane, PCIe, Signal Integrity, Mechanical/Thermal Modeling
What Will Put You Ahead
- Master's Degree in Mechanical Engineering, Electrical/Computer Engineering, Computer Science, Physics or related field
- 5\+ years' experience in designing, qualifying, and/or supporting high speed electrical interconnects and solutions
- Creative problem\-solving ability and independence to solve difficult design constructs.
- Detailed in problem solving and analysis
- Solid understanding of various connectors/cables/solutions manufacturing processes
- Understanding of statistical approach in problem solving, SPC and other quality tools
- Well versed in 2D/3D CAD design tools (NX preferred)
- Deep understanding of chassis level design
For this role, we anticipate paying $150,000\- $190,000 per year. This role is eligible for variable pay, issued as a monetary bonus or in another form .
Hiring Philosophy
All Koch companies value diversity of thought, perspectives, aptitudes, experiences, and backgrounds. We are Military Ready and Second Chance employers. Learn more about our hiring philosophy here .
Who We Are
At Koch, employees are empowered to do what they do best to make life better. Learn how our business philosophy helps employees unleash their potential while creating value for themselves and the company.
Our Benefits
Our goal is for each employee, and their families, to live fulfilling and healthy lives. We provide essential resources and support to build and maintain physical, financial, and emotional strength \- focusing on overall wellbeing so you can focus on what matters most. Our benefits plan includes \- medical, dental, vision, flexible spending and health savings accounts, life insurance, ADD, disability, retirement, paid vacation/time off, educational assistance, and may also include infertility assistance, paid parental leave and adoption assistance. Specific eligibility criteria is set by the applicable Summary Plan Description, policy or guideline and benefits may vary by geographic region. If you have questions on what benefits apply to you, please speak to your recruiter.
Additionally, everyone has individual work and personal needs. We seek to enable the best work environment that helps you and the business work together to produce superior results.
Equal Opportunities
Equal Opportunity Employer, including disability and protected veteran status. Except where prohibited by state law, some offers of employment are conditioned upon successfully passing a drug test. This employer uses E\-Verify. Please click here for additional information. (For Illinois E\-Verify information click here , aquí , or tu ).
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Molex, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($170K) sits 21% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Molex AI Hiring
Molex has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Lisle, IL, US, Seattle, WA, US. Compensation range: $190K - $200K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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