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About This Role
Job Details:
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Job Description:
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About Central Engineering Group (CEG)
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You will join Intel's Central Engineering Group, a dynamic team dedicated to enabling Intel products in artificial intelligence and high\-performance computing domains. This team works collaboratively across engineering disciplines and external partners to research, design, and optimize AI solutions that empower customers and drive innovation. The group plays a critical role in aligning Intel's product offerings with industry trends while delivering solutions that maximize performance and value for Intel's stakeholders.
Position Overview
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As an AI Software Solutions Engineer within the Central Engineering Group, you will play a pivotal role in designing, optimizing, and delivering advanced AI solutions using Intel products. Your day\-to\-day responsibilities will include developing and prototyping AI software, enhancing performance of AI models, and collaborating with cross\-functional teams to tailor solutions that drive adoption of Intel technologies. This hands\-on role offers the opportunity to bring innovative AI concepts to life while contributing to cutting\-edge advancements in the field.
Key Responsibilities
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- Prototype and develop AI software, models, and open\-source libraries to maximize adoption and optimization of Intel products
- Research technical trends and leverage deep expertise in AI frameworks, algorithms, and models to solve complex challenges
- Optimize AI model performance through software adjustments, parameter modifications, and hardware\-specific tuning
- Collaborate with internal engineering teams and external customers to deliver impactful solutions and benchmark collateral
- Partner with AI algorithm and framework engineers to optimize end\-to\-end AI models for Intel hardware features
- Support hardware and software product development teams by contributing to application enablement and product enhancement initiatives
- Serve as a trusted technical advisor, providing technical enabling and guidance to customers and partners
What We're Looking For
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To be successful in this role, you should demonstrate the following professional traits:
- Collaborative Mindset — Works effectively across engineering disciplines and customer\-facing teams to deliver shared outcomes
- Strong Problem\-Solving Abilities — Approaches complex technical challenges with analytical thinking and creative solutions
- Effective Communication — Clearly conveys technical concepts to both engineering peers and external partners
Qualifications:
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Minimum Qualifications* Bachelor's degree in a relevant field with 3\+ years of hands\-on experience in AI, software development, or related domains — OR — a Master's degree in a relevant field
The experience must include:
- Proficiency in programming languages such as Python and related AI software tools
- Foundational knowledge of AI frameworks, algorithms, and models
- Experience conducting code reviews and utilizing software modeling techniques for design and prototyping
Preferred Qualifications* Familiarity with prototyping, proof\-of\-concept design, and AI hardware optimization strategies
- Microsoft tools proficiency
*Experience obtained through internships, academic projects, coursework, or hands\-on training would be highly considered as well.Join the Central Engineering Group to shape the future of AI solutions and make an impact on Intel's cutting\-edge technologies.*Job Type:**
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College Grad
Shift:
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Shift 1 (United States of America)
Primary Location:
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US, Texas, Austin
Additional Locations:
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Business group:
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The Central Engineering Group (CEG) is Intel's data\-driven organization that builds scalable engineering solutions across three pillars: Product Enablement (IP, tools, and methodologies), Custom ASIC (leveraging existing IP for custom silicon), and Foundry Enablement (supporting top customers and validating technologies). The team focuses on customer\-driven, end\-to\-end solutions with short development cycles to deliver measurable business impact across Intel's product and foundry businesses.
Posting Statement:
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All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
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N/A
Benefits
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We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel .
Annual Salary Range for jobs which could be performed in the US: $133,410\.00\-188,340\.00 USD
The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.
Work Model for this Role
This role will be eligible for our hybrid work model which allows employees to split their time between working on\-site at their assigned Intel site and off\-site. \* Job posting details (such as work model, location or time type) are subject to change.
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ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.
Salary Context
This $133K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intel Corporation, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $133K to $188K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Intel Corporation AI Hiring
Intel Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Austin, TX, US, Hillsboro, OR, US. Compensation range: $188K - $330K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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