Interested in this AI/ML Engineer role at MKS?
Apply Now →Skills & Technologies
About This Role
A Day in Your Life:
As the Sr Manager, Strategic Marketing \& AI Enablement you will develop marketing strategy, investment recommendations, market insights, and growth plans to strengthen MKS's market position and profitability, while accelerating practical AI adoption across Strategic Marketing and related functions. You will be reporting to the VP of Strategic Marketing.
This role blends strategic marketing leadership with hands\-on AI enablement. Roughly 50% of the role focuses on market, product, investment, and growth strategy, while the other 50% focuses on identifying, designing, and implementing AI\-enabled workflows, automation, and agentic capabilities that enhance decision\-making, market intelligence, and productivity.
You Will Make an Impact By:
- Support strategic planning for assigned divisions and product lines.
- Develop and execute product, investment, portfolio, and pricing strategies that drive innovation, differentiation, and profitability.
- Analyze market, customer, competitive, technology, and business intelligence to identify trends, opportunities, and growth priorities.
- Create executive\-level market intelligence, including market sizing, competitor assessments, technology roadmaps, and strategic recommendations.
- Support M\&A evaluations, strategic partnerships, and investment decisions through research and analysis.
- Contribute to operating plans and executive, Board, customer, and analyst presentations.
- Partner with business and technology leaders to influence product, customer, market, and technology strategies.
- Collaborate with the Office of the CTO to assess emerging technologies, applications, and innovation opportunities.
AI Enablement and Automation Responsibilities:
- Identify opportunities to apply AI, generative AI, predictive analytics, and automation to improve strategic marketing, market intelligence, and planning processes.
- Design and implement AI\-enabled tools and workflows that enhance the speed, quality, and accessibility of business insights.
- Lead development of AI use cases such as market monitoring, competitive intelligence, document summarization, customer insight analysis, and executive dashboards.
- Partner with cross\-functional teams to define requirements, evaluate solutions, and ensure alignment with business objectives and enterprise standards.
- Drive adoption through training, change management, and responsible AI governance, including data quality, security, and compliance.
- Stay current on emerging AI and automation capabilities, recommending practical applications that deliver measurable business value.
What You Will Bring to the Team:
- Bachelor's degree (Engineering degree is preferred) and 8\+ years of experience in technical product marketing, strategic marketing, corporate strategy, market intelligence, business development, or related roles within technology, industrial, semiconductor, electronics, or advanced manufacturing industries.
- Applicable Advanced degree/mix of coursework/education will be considered as experience.
- 2\+ years of experience with data analytics and visualization tools (e.g., Tableau, Power BI), large language models, and AI\-enabled workflows. Experience with agentic AI is strongly preferred.
- Proven success developing market, product, portfolio, investment, or growth strategies in complex business environments.
- Experience leading AI, automation, analytics, or digital transformation initiatives that improve business performance, decision\-making, or productivity.
- Working knowledge of generative AI, AI agents, workflow automation, business intelligence, and enterprise AI tools, with the ability to balance AI capabilities with human judgment.
- Experience translating business requirements and partnering with IT, data, technical teams, vendors, or consultants to deliver digital and AI\-enabled solutions.
- Familiarity with generative AI platforms, AI agents, large language models, business intelligence tools, data visualization, APIs, Python, and SQL preferred. Deep software engineering expertise is not required.
Other:
- Strong research, analytical, and problem\-solving skills, with the ability to synthesize technical, market, financial, and customer insights into actionable recommendations.
- Strong business acumen and the ability to connect technology trends, market dynamics, customer needs, and financial outcomes.
- Demonstrated leadership and influence skills, with success working across large, complex, matrixed organizations.
- Excellent written, verbal, and presentation skills, with the ability to communicate effectively with technical teams, executives, and customers.
- Proven ability to lead cross\-functional initiatives from concept through implementation, adoption, and continuous improvement.
- Commitment to data integrity, confidentiality, responsible AI practices, and high\-quality business recommendations.
In addition to the above responsibilities, the following are considered material job duties of the position:
- Ability to take and follow directions and instructions.
- Ability to interact with other employees, customers, suppliers, vendors, or the public, in a safe, professional, and respectful manner.
- Access to sensitive and confidential business systems and software, personally identifying information, the company’s financial information, and/or the ability to maintain physical security and safety.
- Because this position involves the above material job duties, trustworthiness, reliability, and good judgment also are material job duties.
MKS is an equal opportunity employer, including disability, veteran status and all categories protected by law. Please review our EOE statements for additional details. MKS is generally only hiring candidates who reside in states where we are registered to do business.
MKS will consider qualified applicants with a criminal history pursuant to the California Fair Chance Act and the Los Angeles County Fair Chance Ordinance for Employers.
Compensation and Benefits:
Salary Pay Range:
Total Base Pay Range $135,000\.00 \- 225,000\.00
per year. This range is a good faith estimate of the expected salary range for this position, based on a wide range of factors including qualifications, experience and training, operational and business needs and other considerations permitted by law.
Bonus: This position is eligible for a discretionary annual bonus, in an amount to be determined by MKS \[or as applicable].
Benefits: MKS offers a comprehensive benefits package, including health insurance coverage (medical, dental and vision), 401(k) with company match, life and disability insurance, 12 paid holidays, sick time, 15 paid vacation days, \[6 weeks fully paid] parental leave, adoption assistance and tuition reimbursement \[and for participation in any stock programs, signing bonus, etc.].
\#LI\-DJ1
Globally, our policy is to recruit individuals from wide and diverse backgrounds. However, certain positions require access to controlled goods and technologies subject to the International Traffic in Arms Regulations (ITAR) or Export Administration Regulations (EAR). Applicants for these positions may need to be “U.S. persons.” “U.S. persons” are generally defined as U.S. citizens, noncitizen nationals, lawful permanent residents (or, green card holders), individuals granted asylum, and individuals admitted as refugees.
MKS Inc. and its affiliates and subsidiaries (“MKS”) is an affirmative action and equal opportunity employer: diverse candidates are encouraged to apply. We win as a team and are committed to recruiting and hiring qualified applicants regardless of race, color, national origin, sex (including pregnancy and pregnancy\-related conditions), religion, age, ancestry, physical or mental disability or handicap, marital status, membership in the uniformed services, veteran status, sexual orientation, gender identity or expression, genetic information, or any other category protected by applicable law. Hiring decisions are based on merit, qualifications and business needs. We conduct background checks and drug screens, in accordance with applicable law and company policies. MKS is generally only hiring candidates who reside in states where we are registered to do business.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
MKS is committed to working with and providing reasonable accommodations to qualified individuals with disabilities. If you need a reasonable accommodation during the application or interview process due to a disability, please contact us at: [email protected] .
If applying for a specific job, please include the requisition number (ex: RXXXX), the title and location of the role
Salary Context
This $135K-$225K range is above 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 MKS, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $135K to $225K.
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.
MKS AI Hiring
MKS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Andover, MA, US. Compensation range: $225K - $225K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.