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About This Role
About MIPS @ GlobalFoundries:
GlobalFoundries is a leading full\-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world’s most inspired technology companies.
With the addition of the MIPS team, GF now offer a rich portfolio of RISC\-V CPUs, especially AI\-enabled processors for the Physical AI world.
Summary of Role:
We are seeking an experienced CPU IP Design Engineer. Responsible for Defining, leading and owning RTL development of our latest AI\-enabled RISC\-V CPU core. The candidate will be responsible for all aspects of the design including Functional Features, Performance, Power, and Area.
Essential Responsibilities:
Drive the micro\-architecture and design of critical blocks of the CPU core
Design of RISC\-V Vector CPU core and its custom extensions
Design of AI\-enabled Matrix engine to augment the Vector CPU
Explore high\-performance strategies working with the CPU modeling team
Perform Microarchitecture development and specification\- from early high\-level architectural exploration, through microarchitectural research and arrive at detailed specifications
Configure Design Features Development, assessment, and refinement of RTL design to target power, performance, area, and timing goals
Perform Functional verification support and assist in the design verification strategy
Assist with the verification of RTL design performance goals
Partner with a multi\-functional engineering team to implement and validate physical design aspects of timing, area, reliability, testability, and power
Other Responsibilities:
Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety \& Security requirements and programs.
Required Qualifications:
Hands\-on working knowledge of the pipeline stages of an in\-order or out\-of\-order high\-performance CPU core
Thorough knowledge of microprocessor architecture including expertise in one or more of the following areas:
- Instruction fetch and decode, branch prediction techniques
- Instruction scheduling, register renaming, Reorder Buffer (ROB)
- Out\-of\-order execution
- Integer and Floating\-point execution
- Load/Store execution
- Instruction and Data Prefetch
- Vector data path
- Cache and memory subsystems
Knowledge of Cache coherency and memory consistency
Knowledge of System Verilog, Verilog and/or VHDL
Experience with simulators and waveform debugging tools.
Knowledge of logic design principles along with timing and power implications
Master’s with 4\-7 years of experience, PhD 2\-5 years of work experience
Preferred Qualifications:
Experience with designing RISC\-V, ARM, and/or MIPS CPU
Experience with Hardware multi\-threading, virtualization, and SIMD designs
Experience with vector and matrix\-enabled CPUs, preferably RISC\-V processors.
Understanding of high\-performance techniques and trade\-offs in a CPU microarchitecture
Understanding of low\-power microarchitecture techniques
Experience using a scripting language such as Perl or Python
Understanding of CPU integration at SoC level
Understanding of Safety and Security microarchitecture
Expected Salary Range
$106,000\.00 \- $205,000\.00
The exact Salary will be determined based on qualifications, experience and location.
If you need a reasonable accommodation for any part of the employment process, please contact us by email at [email protected] and let us know the nature of your request and your contact information. Requests for accommodation will be considered on a case\-by\-case basis. Please note that only inquiries concerning a request for reasonable accommodation will be responded to from this email address.
An offer with GlobalFoundries is conditioned upon the successful completion of pre\-employment conditions, as applicable, and subject to applicable laws and regulations.
GlobalFoundries is fully committed to equal opportunity in the workplace and believes that cultural diversity within the company enhances its business potential. GlobalFoundries goal of excellence in business necessitates the attraction and retention of highly qualified people. Artificial barriers and stereotypic biases detract from this objective and may be illegally discriminatory.
All policies and processes which pertain to employees including recruitment, selection, training, utilization, promotion, compensation, benefits, extracurricular programs, and termination are created and implemented without regard to age, ethnicity, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third\-party affiliation, sex, sexual orientation, gender identity or expression, veteran status, or any other characteristic or category specified by local, state or federal law
Salary Context
This $106K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At GlobalFoundries, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 14% below the category median. Disclosed range: $106K to $205K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
GlobalFoundries AI Hiring
GlobalFoundries has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Austin, TX, US. Compensation range: $184K - $325K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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