Interested in this AI/ML Engineer role at Element Solutions Inc?
Apply Now →Skills & Technologies
About This Role
Challenge Yourself and Impact the Future!
MacDermid Alpha Electronics Solutions, a business of Element Solutions Inc (NYSE: ESI), is renowned worldwide for its commitment to revolutionizing the electronics industry. With a legacy spanning over a century, we have continually set new benchmarks for excellence, reliability and sustainability in electronic materials.
Our Expertise:
Wafer Level Solutions - Revolutionizing wafer fabrication processes for enhanced efficiency and performance
Semiconductor Assembly Solutions - Driving innovation in semiconductor assembly processes for unparallelled reliability
Circuitry Solutions - Tailored solutions to meet the dynamic demands of modern circuitry
Circuit Board Assembly Solutions - Elevating circuit board assembly processes for optimal performance
Film & Smart Surface Solutions - Transforming electronics with cutting-edge materials and technologies for enhanced functionality and reliability
Across diverse sectors including automotive, consumer electronics, mobile devices, telecom, data storage, and infrastructure, MacDermid Alpha Electronics Solutions has earned the trust of manufacturers worldwide. Our comprehensive range of high quality solutions and technical services covers the entire electronics supply chain, empowering businesses to thrive in today's competitive landscape.
We strive to embody the five 'Elements of our Culture'- our '5C's'; Challenge, Commit, Collaborate, Choose, and Care. These core values are the foundation of our organization which our employees embrace in their interactions with customers, colleagues and other stakeholders, to drive financial performance and create a rewarding work environment.
Who are we looking for?
---------------------------
Reporting into the SVP – MAES Operations with hard matrix reporting to the SVP PBA, the VP of Supply Chain – PBA will lead the Supply Chain function for the three business units within the PBA division. The preferred candidate will have full ownership and responsibility for all supply chain plants across the globe, supporting three distinct business units. The VP of Supply Chain PBA will partner closely as a member of the PBA business leadership team, and a key resource to enable successful growth for a ~900 employees and $1.1B enterprise. This individual will lead CI cultural transformation, as our team moves towards a continuous improvement mindset in everything we do.
What will you be doing?
---------------------------
- Ownership of all global Manufacturing/Supply Chain activities from procurement to delivery of finished goods and strategic initiatives that drive PBA growth and performance against financial commitments.
- Effectively leverages people, processes, capabilities, and technologies in partnership with the business and technology partners.
- Continuously improve operations and business processes through development and alignment of MAES and PBA continuous improvement with a strong focus on establishing culture that incorporates CI and lean concepts to deliver a consistent standard of processes for safety, material management, sourcing, and cost out.
- Deploys resources to achieve strategic objectives and directly shapes strategy and drives changes throughout the global Supply Chain team.
- Develops and owns supply, inventory, operations planning process globally in coordination with ISC business unit partners through SIOP processes.
- Ownership of global safety and EHS commitments in partnership with ISC business unit partners and the PBA leadership team
- Defines, leads, and executes global manufacturing requirements and metrics.
- Create synergies through key performance indicators within manufacturing operations across the regions to maximize manufacturing profit.
- Partner with Quality leadership to develop solutions that drive the function forward and proactively meet customer needs.
- Acts as a partner to NPD with Product Managers and in conjunction with other members of the leadership team
- Is the ultimate owner of Supply Chain escalations and priority issue resolution in partnership with Business Unit supply chain partners.
- Develop, maintain, and execute a global capital planning process with emphasis on a 3 year time horizon
Who are You?
----------------
- 4-year degree in Supply Chain, Management, Operations, or another technical discipline required
- Advanced degree such as an MBA is preferred.
- Minimum of 15 years dedicated operations experience with progressive growth in responsibilities and ownership.
- Demonstrated track record of Continuous Improvement experience and deep domain expertise.
- Senior management experience running large (500+ employee or more) organizations.
- Global experience and ability to effectively communicate across various cultures.
- Willingness to travel up to 50% of the time depending on business need.
We understand that not all candidates may meet the requirements listed above. If you believe you have the skills and experience necessary to excel in this role, we encourage you to apply.
What competencies will you need?
------------------------------------
- Ability to drive accountability for results yet be supportive and effective at developing individuals to assume greater levels of responsibility and personal contribution. Lead, motivate, and develop the functional team on an ongoing basis by providing direct feedback, counsel, and coaching to drive consistent practices and processes aligned to overall company performance.
- Knowledge and experience in a Lean or Continuous Improvement Operating system that has been demonstrated over time and in multiple contexts.
- Executive presence and ability to effectively inspire and develop a global team.
We are Offering...
----------------------
Challenge Yourself and Impact the Future - We are committed to solving the complete and evolving needs of our customers through innovation and high-quality standards. We are focused on brining cutting edge and environmentally sustainable solutions to the market. Our people are the critical resource required to make that happen. We support your success by creating a strong, inclusive culture, competitive total rewards and an appropriate work-life balance.
As part of the MAES Team, you will have ...
- Opportunities for career growth, competitive compensation (competitive base salary and performance related bonus plan) and benefits packages (health, dental, and vision insurance, Wellness Program, PTO/Holidays, as well as a 401(k)-retirement plan with a company match).
- The typical base salary range for this position is between $223,200 to $279,025 annually.
- Innovated work environment where you will be a part of a dynamic and collaborative team.
- Perks and Incentives such as paid parental leave, tuition reimbursement, and opportunities for professional development. #LI-ED1
Equal Opportunity Employer
All qualified applications will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category applicable under federal, state and local laws.
Salary Context
This $223K-$279K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Element Solutions Inc, 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 $154,000 based on 8,743 positions with disclosed compensation. This role's midpoint ($251K) sits 63% above the category median. Disclosed range: $223K to $279K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Element Solutions Inc AI Hiring
Element Solutions Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Piscataway, NJ, US, Norwalk, CT, US. Compensation range: $211K - $279K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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.