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About This Role
Job Title: Machine Learning Modeling Winter/Spring Co-Op (Jan-June '27) Posting Start Date: 10/7/26 Job Location(s): Irvine If you are looking for a challenging and exciting career in the world of technology, then look no further. Skyworks is an innovator of high-performance analog semiconductors whose solutions are powering the wireless networking revolution. Through our broad technology expertise and one of the most extensive product portfolios in the industry, we are Connecting Everyone and Everything, All the Time.
At Skyworks, you will find a fast-paced environment with a strong focus on global collaboration, minimal layers of management, and the freedom to make meaningful contributions in a setting that encourages creative thinking. We are excited about the opportunity to work with you and glad you want to be part of a team of talented individuals who together are changing the way the world communicates.
Requisition ID: 78575
Job Description: Description
Skyworks is a global leader in wireless connectivity, delivering cutting-edge RF, acoustic wave, and semiconductor technologies that power next-generation communication systems. As we continue to expand our artificial intelligence and machine learning capabilities, we are leveraging advanced analytics and data-driven approaches to accelerate innovation, optimize manufacturing processes, and enhance product performance.
We are seeking a highly motivated Machine Learning Modeling Engineer Intern to join our Acoustic Device Group. In this role, you will develop, implement, and optimize machine learning models to support the design, analysis, and development of advanced acoustic and RF devices. You will work with complex engineering datasets and collaborate closely with multidisciplinary teams to apply AI and machine learning techniques to real-world semiconductor challenges.
The ideal candidate has strong analytical and problem-solving skills, a solid foundation in machine learning algorithms and data science, and a passion for applying AI technologies to RF, acoustic wave, and semiconductor engineering. This position offers a unique opportunity to contribute to next-generation wireless products while gaining hands-on experience at the intersection of machine learning and advanced semiconductor technology.
Co-Op Period from January to June 2027
Responsibilities
- Apply artificial neural network (ANN) and machine learning techniques to support microwave CAD and semiconductor design projects.
- Develop and evaluate ANN-based models for acoustic devices, electromagnetic (EM) analysis, and parametric modeling.
- Assist in the development of data-driven models for RF filter design and performance prediction.
- Support neural network-based multiphysics modeling of electrical, acoustic, thermal, and mechanical interactions.
- Collect, clean, and preprocess structured, unstructured, time-series, and image datasets for machine learning applications.
- Perform exploratory data analysis (EDA), feature engineering, and data visualization to improve model accuracy and insights.
- Build and optimize LLM-powered agents with capabilities such as tool integration, workflow automation, and multi-step reasoning.
- Research emerging developments in AI, machine learning, and data science, and explore their applications in semiconductor design and manufacturing optimization.
Required Experience and Skills
- Currently pursuing an M.S. or Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics, Data Science, or a related technical field.
- Solid foundation in machine learning, deep learning, statistical modeling, and data analysis techniques.
- Proficiency in Python programming; experience with C++ and SQL is a plus.
- Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Keras.
- Familiarity with data analysis and scientific computing libraries, including NumPy, Pandas, and Scikit-learn.
- Basic knowledge of cloud computing platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Experience developing machine learning workflows, data pipelines, or scalable software solutions through coursework, research, or projects.
- Strong analytical and problem-solving skills, with attention to detail and the ability to work independently.
- Self-motivated learner with a passion for artificial intelligence, innovation, and continuous professional growth.
Desired Experience and Skills
- Coursework or project experience in large language models (LLMs), generative AI, agent-based systems, or deep learning applications
- Coursework or project experience in RF, microwave engineering, signal processing, computational electromagnetics, or semiconductor devices
- Experience working with simulation-generated datasets and physics-based models: knowledge of Physics-Informed Neural Networks (PINNs), surrogate modeling, or digital twin methodologies.
- Familiarity with scientific computing, numerical optimization, and surrogate modeling techniques.
- Familiarity with reinforcement learning, Bayesian optimization, or other optimization techniques for engineering applications.
- Interest in applying AI and machine learning techniques to engineering design automation, semiconductor technologies, and manufacturing optimization.
- Familiarity with Git, Jira, and MLflow for version control and model management.
The typical pay range for an Engineering intern across the U.S. is currently USD $29.00 - $49.00 per hour and for a Non-Engineering intern across the U.S. is currently USD $23.00 - $42.00 per hour. Starting pay will depend on level of education, the ultimate job duties and requirements, and work location. Skyworks has different pay ranges for different work locations in the U.S.
Skyworks is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Skyworks strives to create an accessible workplace; if you need an accommodation due to a disability, please contact us at [email protected].
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Salary Context
This $47K-$101K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Skyworks Solutions, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($74K) sits 65% below the category median. Disclosed range: $47K to $101K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Skyworks Solutions AI Hiring
Skyworks Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Irvine, CA, US. Compensation range: $101K - $255K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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