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About This Role
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Advancing the World's Technology Together
Our technology solutions power the tools you use every day-including smartphones, electric vehicles, hyperscale data centers, IoT devices, and so much more. Here, you'll have an opportunity to be part of a global leader whose innovative designs are pushing the boundaries of what's possible and powering the future.
We believe innovation and growth are driven by an inclusive culture and a diverse workforce. We're dedicated to empowering people to be their true selves. Together, we're building a better tomorrow for our employees, customers, partners, and communities.
Principal Engineer, Architecture & Performance Research Engineer for Data Center and Agentic AI CPU
What You'll Do
Architecture Research Lab is focused on researching next-generation CPU (RISC-V) microarchitecture and performance for emerging computing workloads. We explore new architectural ideas, evaluate their performance potential, and rapidly turn promising concepts into working designs that can be validated on real silicon. Our research is driven by emerging opportunities in data center and agentic AI workloads, where we investigate how CPU architecture can evolve to meet new performance and efficiency requirements. We also explore CPU-memory co-design from the CPU perspective, in alignment with the broader shift toward memory-centric architectures. A distinctive aspect of our team is the way we approach architecture research. We develop and apply AI-driven methodologies to accelerate architectural exploration, broaden the design space we can investigate, and quickly iterate from ideas to implementation and silicon validation. Rather than simply following established design flows, we continuously seek new ways to explore, evaluate, and realize CPU architectures. The role offers an opportunity to work across the full spectrum of architecture research—from workload and performance analysis, microarchitectural innovation, and architectural modeling to implementation and silicon validation—while helping shape new methodologies for how future CPUs are designed.
Location: Daily onsite presence at our San Jose office in alignment with our Flexible Work policy
Job ID: 43044
As a CPU Architecture & Performance Engineer, you will define the architectural vision and performance direction for next-generation CPU platforms. You will identify opportunities across emerging workloads, shape long-term architectural strategies, develop and refine performance models, and drive new microarchitectural concepts across multiple architectural domains. You will work closely with RTL design teams and cross-functional engineering partners to translate architectural vision into implementable designs, influence key technical decisions, and validate architectural choices from modeling through silicon. The role carries broad technical ownership, with the opportunity to establish new research directions, drive architecture across multiple generations of CPUs, and shape the fundamental design choices that determine future CPU performance and capabilities.
- Define the application-specialization direction for next-generation CPU cores, including agentic AI and memory-centric architectures, and lead the overall microarchitectural vision
- Lead end-to-end performance analysis using architectural simulators and RTL, guiding key architectural trade-offs and decisions
- Translate workload characterization and performance insights into architectural requirements and long-term design direction
- Drive architecture-to-implementation alignment with design teams, ensuring architectural intent is carried through RTL and silicon
- Mentor Senior and Staff engineers and provide technical leadership across multiple projects and architectural domains
- Drive research and technical contributions leading to patents and publications
What You Bring
- Master's degree in Computer Engineering, Computer Science, or a related field with 18+ years of relevant experience, or PhD with 15+ years of relevant experience
- 5+ years of experience in CPU microarchitecture, architecture research, or performance engineering, with a demonstrated track record of driving architectural direction
- Extensive experience designing and evaluating high-performance CPU architectures, including RISC-V, ARM, or x86 cores; experience with GPU/NPU vector or accelerator architectures is also relevant
- Proven ability to use quantitative performance analysis and modeling to identify architectural opportunities, evaluate system-level trade-offs, and influence major microarchitectural decisions
- Deep expertise in at least one major microarchitectural domain, with the ability to drive architectural direction and technical decisions in that area:
- + Front-end: Branch prediction, fetch/decode, instruction delivery, and decoupled front-end architectures + Mid-end: Out-of-order execution, scheduling, RVV/SIMD, vector execution, or matrix/tensor acceleration + Back-end: Load/store units, MSHRs, cache and memory systems, prefetching, or irregular memory access optimization
- Hands-on experience building, extending, and applying architectural simulators and performance models, such as gem5, to guide architecture decisions
- Strong programming skills in C/C++ and Python, with experience developing tools and automation for performance analysis and architectural exploration
- Demonstrated ability to take architectural concepts from research and modeling through RTL implementation and silicon validation
Preferred Qualifications
- Experience defining or influencing CPU architecture for emerging workloads, particularly AI/ML, agentic AI, or data center applications
- Experience with AI-driven architecture and design methodologies, including AI-assisted architecture exploration, high-level design, or RTL generation
- Strong perspective on memory-centric architectures and CPU-memory co-design, with experience translating memory-system characteristics into CPU architectural opportunities
- Experience driving system-level PPA trade-offs across performance, power, area, and implementation complexity
- Experience taking architectural concepts through RTL implementation, tape-out, and silicon validation
- Experience leading cross-functional architecture initiatives and influencing technical direction across multiple engineering teams
- Proven ability to mentor and develop senior technical engineers and shape architecture research across multiple domains
- Track record of impactful architectural innovation through patents, publications, or deployed products
- You're inclusive, adapting your style to the situation and diverse global norms of our people.
- You approach challenges with curiosity and resilience, seeking data to help build understanding.
- You're collaborative, building relationships, humbly offering support and openly welcoming approaches.
- Innovative and creative, you proactively explore new ideas and adapt quickly to change
#LI-SF1
Equal Opportunity Employment Policy
Samsung Semiconductor takes pride in being an equal opportunity workplace dedicated to fostering an environment where all individuals feel valued and empowered to excel, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status.
When selecting team members, we prioritize talent and qualities such as humility, kindness, and dedication. We extend comprehensive accommodations throughout our recruiting processes for candidates with disabilities, long-term conditions, neurodivergent individuals, or those requiring pregnancy-related support. All candidates scheduled for an interview will receive guidance on requesting accommodations.
Our Commitment to Innovation and Fairness
At Samsung Semiconductor, we use Artificial Intelligence (AI) tools in the recruitment process to enhance efficiency. However, AI is used as a support tool, not a final decision-maker. All hiring decisions are made by our human recruiting team and hiring managers to ensure every candidate is evaluated fairly and holistically.
Recruiting Agency Policy
We do not accept unsolicited resumes. Only authorized recruitment agencies that have a current and valid agreement with Samsung Semiconductor, Inc. are permitted to submit resumes for any job openings.
Applicant AI Use Policy
At Samsung Semiconductor, we support innovation and technology. However, to ensure a fair and authentic assessment, we ask that candidates rely on their own knowledge and skills throughout the process. AI tools may be used for basic preparation, grammar, and research, but should not be used to generate or assist with submitted content or live interview responses. If we determine that AI is being used outside these guidelines, we reserve the right to pause or end the interview, and your candidacy may be disqualified.
Trade Secret Notice
By submitting an application, you agree not to disclose to Samsung—or encourage Samsung to use—any confidential or proprietary information (including trade secrets) belonging to a current or former employer or other entity.
Applicant Privacy Policy
https://semiconductor.samsung.com/about-us/careers/us/privacy/
Salary Context
This $219K-$351K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Samsung Semiconductor Inc (US), 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($285K) sits 34% above the category median. Disclosed range: $219K to $351K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Samsung Semiconductor Inc (US) AI Hiring
Samsung Semiconductor Inc (US) has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $253K - $351K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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