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AboutGlobalFoundries
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 a global manufacturing footprint spanning three continents, GlobalFoundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit www.gf.com .
About the Role
We're looking for a seasoned AI/ML Staff Software Engineer to lead workload\-driven architecture strategy across hardware and software boundaries. You will define how we study, model, and optimize AI/ML workloads for current and next\-generation products, drive alignment across HW and SW engineering organizations, and serve as a technical authority on performance and architecture tradeoffs. This is a senior individual contributor role with significant cross\-functional scope and organizational influence.
Summary of Role
We're looking for a seasoned AI/ML Staff Software Engineer to lead workload\-driven architecture strategy across hardware and software boundaries. You will define how we study, model, and optimize AI/ML workloads for current and next\-generation products, drive alignment across HW and SW engineering organizations, and serve as a technical authority on performance and architecture tradeoffs. This is a senior individual contributor role with significant cross\-functional scope and organizational influence.
Essential Responsibilities
Own workload characterization and hardware performance analysis for AI/ML systems — selecting representative workloads, defining measurement methodology, building support for MIPS products (e.g., the S8200\), and projecting system\-level KPIs. Your findings will directly inform SoC architecture decisions, memory subsystem design, and HW/SW co\-optimization strategy.
Define the software frameworks across the product portfolio: what metrics matter, how to measure them accurately, how to estimate them pre\-silicon, and how to use them to make architectural bets. Leverage open\-source infrastructure like MLIR and IREE to implement and validate this work. Set the standard for how the team approaches this and mentor junior engineers in applying it.
Represent software in architectural discussions with hardware teams (CPU, SoC, memory, interconnect) and software teams (compilers, runtimes, ML frameworks). Identify critical bottlenecks — compute throughput, DRAM bandwidth, on\-chip memory, data movement latency, or software overhead — and build the case for specific architectural changes or optimization investments.
Present findings and recommendations to senior engineering leadership and product stakeholders. You should be as comfortable writing a one\-page architectural recommendation as a detailed technical memo.
Other Responsibilities:
- Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety \& Security requirements and programs.
Required Qualifications:
BS or MS (preferred) in EE, CE, CS, or equivalent, with 5\+ years in systems engineering, hardware architecture, ML systems, or performance engineering, and a track record of technical leadership.
Deep expertise in CPU and SoC architecture — memory hierarchies, out\-of\-order execution, vector/SIMD pipelines, power management — and how these interact with AI/ML workloads. Strong command of system\-level memory bandwidth constraints (DDR/LPDDR bandwidth, channel configuration, utilization efficiency) and the ability to reason quantitatively about memory\-bound vs. compute\-bound workloads.
Experience with AI/ML acceleration on edge devices — NPUs, dedicated inference accelerators, DSP\-based pipelines — and the HW/SW co\-design challenges involved. Familiarity with model quantization, sparsity, or other efficiency techniques and their hardware interaction is a strong plus.
Familiarity with AI compiler infrastructure: MLIR\-based toolchains, IREE, TVM, TFLite, or equivalent. Understanding how graph representations are transformed, tiled, scheduled, and lowered to hardware will improve your ability to identify where compiler strategy and hardware architecture must be co\-designed. Prior contributions to such toolchains are a significant differentiator.
Effective cross\-functional collaborator who can drive technical consensus without direct authority, writes clearly, and calibrates technical depth for different audiences.
Preferred Qualifications
- Prior implementation of CPU hardware features such as vector extensions (AVX, NEON, RVV) or matrix extensions (AMX, SME)
- Experience defining or co\-defining SoC architecture requirements from workload analysis
- Contributions to graph lowering in MLIR/IREE or similar compiler infrastructure
- Internal or external publications or contributions to technical standards
- Experience mentoring junior systems engineers
- Knowledge of RISC\-V architecture and Vector/Matrix extensions
Other Requirements
- English fluency (written and verbal)
- Up to 10% travel
- US work authorization
- 100% in\-office (Dallas, Austin, or San Jose)
G lobal F oundries is an equal opportunity employer, cultivating a diverse and inclusive workforce. We believe having a multicultural workplace enhances productivity, efficiency and innovation whilst our employees feel truly respected, valued and heard.
As an affirmative employer, all qualified applicants are considered for employment regardless of age, ethnicity, marital status, citizenship, race, religion, political affiliation, gender, sexual orientation and medical and/or physical abilities.
All offers of employment with GlobalFoundries are conditioned upon the successful completion of background checks , medical screenings as applicable and subject to the respective local laws and regulations.
Expected Salary Range
$106,000\.00 \- $184,000\.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-$184K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At GlobalFoundries, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 38% below the category median. Disclosed range: $106K to $184K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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