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About This Role
Job Title:
Specialist, AI EngineerJob Description:
The Role
Entegris is seeking a Junior AI Engineer to join our IT Digital Platform AI team. In this role, you’ll contribute to AI powered solutions while building hands on experience with modern machine learning and generative AI patterns (e.g., retrieval\-augmented generation, tool calling, and workflow orchestration) on cloud\-based platforms.
You’ll work closely with senior engineers, data teams, and business stakeholders to help design, build, test, and deploy AI solutions that address real business challenges. You’ll start with well scoped tasks and grow your ownership over time through mentorship, pairing, and code reviews.
Success in This Role
- Delivers reliable, readable code with guidance, incorporating feedback from code reviews.
- Breaks down requirements into clear tasks, communicates progress early, and asks questions when blocked.
- Contributes to small to medium features (e.g., data prep, API integrations, evaluation scripts) and gradually increases ownership.
- Practices quality basics: unit tests where appropriate, clear documentation, and safe handling of sensitive data.
- Learns platform standards (security, privacy, and cost awareness) and applies them consistently.
- Demonstrates curiosity and learning, measuring outcomes, and sharing takeaways with the team.
What You’ll Do
Development \& Engineering
- Support the design, development, and deployment of AI solutions, including LLM based and agent driven applications, with guidance from senior engineers.
- Write, test, and maintain code for AI/ML solutions using Python and modern frameworks.
- Assist in building and integrating AI agents and services within the enterprise platform.
- Contribute to prompt development, evaluation, and basic observability practices (e.g., logging, metrics) with guidance and established patterns.
Platform \& Collaboration
- Work within the enterprise AI platform, learning and following established architecture patterns, development standards, and secure coding practices.
- Collaborate with Cloud, Data, and Security teams to implement solutions aligned with enterprise guidelines.
- Participate in team discussions, sprint planning, and code reviews to continuously improve delivery quality.
Delivery \& Learning
- Help translate business requirements into technical tasks and AI driven capabilities.
- Support the deployment and monitoring of AI solutions in development and production environments.
- Learn and apply best practices for scalability, security, and cost\-efficient AI solutions.
Growth \& Mentorship
- Learn from senior engineers through mentorship, pairing, and code reviews.
- Continuously build knowledge in AI/ML, agentic systems, and cloud technologies.
- Contribute to a culture of learning, collaboration, and continuous improvement.
Responsible AI
- Follow established guidelines for responsible AI, including security, privacy, and ethical use.
- Support implementation of guardrails and governance practices defined by the platform.
Tech Stack \& Tools
- Languages \& fundamentals: Python, REST APIs, Git, and basic CI/CD concepts
- AI/ML \& GenAI: model evaluation basics, prompt development, retrieval\-augmented generation (RAG), and agent/workflow orchestration concepts
- Cloud (preferred): Google Cloud Platform (GCP) and services such as Vertex AI, BigQuery, and Dataflow (depending on project needs)
- Quality \& operations: testing, logging/metrics, and basic monitoring for services in development and production
- Nice to have: Terraform (or other infrastructure\-as\-code), containers, and data pipelines (ETL/ELT)
What We Seek
Required Skills
- Bachelor’s degree (completed or in progress) in Computer Science, AI, ML, or a related field, or equivalent experience; 0–2 years of experience (internships/co\-ops/projects welcome).
- Foundational knowledge of Python and basic AI/ML concepts.
- Familiarity with cloud platforms (GCP preferred but not required).
- Understanding of software development fundamentals (Git, version control, basic CI/CD concepts).
- Strong willingness to learn new technologies and problem\-solve in a team environment.
- Good communication skills and the ability to collaborate with technical and non\-technical stakeholders.
Preferred Skills
- Exposure to LLMs, prompt engineering, or generative AI concepts.
- Basic understanding of agentic AI concepts or orchestration patterns.
- Familiarity with GCP services such as Vertex AI, BigQuery, or Dataflow.
- Experience with REST APIs and integrating external services.
- Awareness of data processing concepts (ETL/ELT).
- Exposure to infrastructure\-as\-code tools (e.g., Terraform) is a plus.
Why Work at Entegris
Lead. Inspire. Innovate. Define Your Future.
Not everyone who works for a global company shares the same background, experiences, and perspectives. We leverage the differences of our employees to bring new ideas to the table. Every employee throughout the company is encouraged to share input on projects and initiatives. Our decision\-making process is truly a collaborative effort as we realize there are leaders at every level of the organization. We put our values at the core of how we operate as an organization—not just when it’s convenient, but in a lasting and meaningful way. We want the time and energy you spend here to have a positive impact on your life inside and outside of the office.
What We Offer
Our total rewards package goes above and beyond just a paycheck. Whether you’re looking to build your career, improve your health, or protect your wealth, we offer generous benefits to help you achieve your goals.
- Compensation: $83,500 \- $104,500 per year range with actual pay dependent on candidate overall skills for the role
- Annual bonus eligibility
- Progressive paid time off policy that empowers you to take the time you need to recharge
- Generous 401(K) plan with an impressive employer match with no delayed vesting
- Excellent health, dental and vision insurance packages to fit your needs
- Education assistance to support your learning journey
- A values\-driven culture with colleagues that rally around People, Accountability, Creativity and Excellence
*Entegris does not provide immigration\-related sponsorship for this role. Do not apply for this role if you will need Entegris immigration sponsorship (e.g., H1B, TN, STEM OPT, etc.) now or in the future.*
*At Entegris we are committed to providing equal opportunity to all employees and applicants. Our policy is to recruit, hire, train, and reward employees for their individual abilities, achievements, and experience without regard to race, color, religion, sexual orientation, age, national origin, disability, marital or military status.*
\#LI\-MW1
Salary Context
This $83K-$104K range is in the lower quartile 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 Entegris, 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 ($94K) sits 56% below the category median. Disclosed range: $83K to $104K.
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.
Entegris AI Hiring
Entegris has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bedford, MA, US. Compensation range: $104K - $104K.
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
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