OPT Performance Engineer Jobs
Performance Engineer jobs are a strong fit for F-1 OPT students with backgrounds in computer science, systems engineering, or software engineering. Most roles qualify as STEM OPT, giving you up to 36 months of work authorization. Employers in cloud infrastructure, fintech, and enterprise software regularly sponsor H-1B visas for this role.
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Get Access To All JobsINTRODUCTION
Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
The engineer will work with senior engineers and researchers on AI training and inference systems, with a strong focus on LLM execution engines, data and KV‑cache management, and multi‑tier memory hierarchies across modern data‑center platforms. The role centers on end‑to‑end performance characterization and optimization of large‑scale AI workloads, spanning single‑node GPUs to rack‑scale inference deployments.
Responsibilities include systems software development, workload engineering, performance analysis, and memory‑centric optimization for LLM training, serving, and agentic AI frameworks. The work emphasizes real customer inference and training workloads, emerging memory technologies (HBM, LP/DRAM, CXL, NVMe, remote memory fabrics), and the economics and token‑level efficiency of large‑scale inference systems.
This role combines hands‑on engineering with applied systems research, directly influencing next‑generation AI platforms and memory‑driven system architectures.
KEY RESPONSIBILITIES
- Build, develop, and improve systems software tools for profiling, tracing, and analyzing LLM training and inference workloads
- Design and evaluate KV‑cache and state‑management strategies for LLM serving, including reuse, eviction, compression, tiering, and lifecycle management
- Build and extend benchmarking, simulation, and emulation frameworks for AI inference and training across heterogeneous memory tiers
- Develop and evaluate data placement, migration, and prefetching algorithms across HBM, LP/DRAM, CXL memory pools, NVMe, and remote memory systems
- Characterize and optimize LLM execution engines (prefill/decode), including attention behavior, batching strategies, and token‑level performance
- Analyze rack‑scale and cluster‑scale inference deployments, focusing on throughput, latency, utilization, cost, and token economics
- Develop workloads that reflect real customer AI systems, including LLM serving, agentic pipelines, retrieval‑augmented generation, multimodal inference, and long‑context workloads
- Instrument and analyze performance across GPUs, CPUs, memory subsystems, interconnects, and storage, identifying end‑to‑end bottlenecks
- Evaluate system interactions across OS, runtime layers, containerized deployments, and distributed inference stacks
- Automate performance measurement, experimentation, and analysis workflows to improve repeatability and scale
- Summarize findings into clear methodologies, internal reports, and technical presentations for engineering and leadership audiences
- Collaborate across engineering, architecture, and research teams, and with external academic and industry partners
- Provide actionable feedback to product, architecture, and platform teams to influence future AI systems and memory designs
REQUIRED QUALIFICATIONS
- Bachelor’s or Master’s degree, or equivalent experience, in Computer Science, Electrical Engineering, or a related field
- Strong foundation in operating systems, memory systems, parallel computing, or distributed systems
- Proficiency in systems programming and analysis using C/C++ and Python
- Experience working in Linux environments, including debugging, profiling, and automation
- Solid understanding of modern server architectures, including GPUs, CPUs, cache hierarchies, NUMA, and memory subsystems
- Experience analyzing performance data and reasoning about system‑level behavior
- Strong written and verbal communication skills
- Ability to work independently on scoped problems and collaboratively on larger system efforts
PREFERRED QUALIFICATIONS
- Experience with LLM training and inference systems, including execution runtimes and serving frameworks
- Hands‑on experience with KV cache management, long‑context execution, or stateful inference workloads
- Familiarity with GPU architectures and AI accelerators, including memory and interconnect behavior
- Experience with multi‑tier memory systems, including HBM, LP/DRAM, CXL‑attached memory, NVMe, and remote/disaggregated memory
- Experience profiling and optimizing AI inference pipelines, including batching, scheduling, and latency‑sensitive workloads
- Familiarity with agentic AI frameworks, multi‑agent systems, or workflow‑based inference pipelines
- Experience with distributed AI systems, rack‑scale deployments, or cluster‑level performance analysis
- Exposure to memory or system simulators (e.g., gem5, Ramulator) or analytical performance modeling
- Familiarity with containers, orchestration, and AI infrastructure stacks
- Experience applying machine learning techniques to systems optimization or performance analysis
As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth. Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future. We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget. Micron also provides benefit programs that help protect your income if you are unable to work due to illness or injury, and paid family leave. Additionally, Micron benefits include a robust paid time-off program and paid holidays. For additional information regarding the Benefit programs available, please see the Benefits Guide posted on micron.com/careers/benefits.
Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, citizenship status, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
To learn more about Micron, please visit micron.com/careers
For US Sites Only: To request assistance with the application process and/or for reasonable accommodations, please contact Micron’s People Organization at hrsupport_na@micron.com or 1-800-336-8918 (select option #3)
Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.
Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.
AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.
Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.
INTRODUCTION
Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
The engineer will work with senior engineers and researchers on AI training and inference systems, with a strong focus on LLM execution engines, data and KV‑cache management, and multi‑tier memory hierarchies across modern data‑center platforms. The role centers on end‑to‑end performance characterization and optimization of large‑scale AI workloads, spanning single‑node GPUs to rack‑scale inference deployments.
Responsibilities include systems software development, workload engineering, performance analysis, and memory‑centric optimization for LLM training, serving, and agentic AI frameworks. The work emphasizes real customer inference and training workloads, emerging memory technologies (HBM, LP/DRAM, CXL, NVMe, remote memory fabrics), and the economics and token‑level efficiency of large‑scale inference systems.
This role combines hands‑on engineering with applied systems research, directly influencing next‑generation AI platforms and memory‑driven system architectures.
KEY RESPONSIBILITIES
- Build, develop, and improve systems software tools for profiling, tracing, and analyzing LLM training and inference workloads
- Design and evaluate KV‑cache and state‑management strategies for LLM serving, including reuse, eviction, compression, tiering, and lifecycle management
- Build and extend benchmarking, simulation, and emulation frameworks for AI inference and training across heterogeneous memory tiers
- Develop and evaluate data placement, migration, and prefetching algorithms across HBM, LP/DRAM, CXL memory pools, NVMe, and remote memory systems
- Characterize and optimize LLM execution engines (prefill/decode), including attention behavior, batching strategies, and token‑level performance
- Analyze rack‑scale and cluster‑scale inference deployments, focusing on throughput, latency, utilization, cost, and token economics
- Develop workloads that reflect real customer AI systems, including LLM serving, agentic pipelines, retrieval‑augmented generation, multimodal inference, and long‑context workloads
- Instrument and analyze performance across GPUs, CPUs, memory subsystems, interconnects, and storage, identifying end‑to‑end bottlenecks
- Evaluate system interactions across OS, runtime layers, containerized deployments, and distributed inference stacks
- Automate performance measurement, experimentation, and analysis workflows to improve repeatability and scale
- Summarize findings into clear methodologies, internal reports, and technical presentations for engineering and leadership audiences
- Collaborate across engineering, architecture, and research teams, and with external academic and industry partners
- Provide actionable feedback to product, architecture, and platform teams to influence future AI systems and memory designs
REQUIRED QUALIFICATIONS
- Bachelor’s or Master’s degree, or equivalent experience, in Computer Science, Electrical Engineering, or a related field
- Strong foundation in operating systems, memory systems, parallel computing, or distributed systems
- Proficiency in systems programming and analysis using C/C++ and Python
- Experience working in Linux environments, including debugging, profiling, and automation
- Solid understanding of modern server architectures, including GPUs, CPUs, cache hierarchies, NUMA, and memory subsystems
- Experience analyzing performance data and reasoning about system‑level behavior
- Strong written and verbal communication skills
- Ability to work independently on scoped problems and collaboratively on larger system efforts
PREFERRED QUALIFICATIONS
- Experience with LLM training and inference systems, including execution runtimes and serving frameworks
- Hands‑on experience with KV cache management, long‑context execution, or stateful inference workloads
- Familiarity with GPU architectures and AI accelerators, including memory and interconnect behavior
- Experience with multi‑tier memory systems, including HBM, LP/DRAM, CXL‑attached memory, NVMe, and remote/disaggregated memory
- Experience profiling and optimizing AI inference pipelines, including batching, scheduling, and latency‑sensitive workloads
- Familiarity with agentic AI frameworks, multi‑agent systems, or workflow‑based inference pipelines
- Experience with distributed AI systems, rack‑scale deployments, or cluster‑level performance analysis
- Exposure to memory or system simulators (e.g., gem5, Ramulator) or analytical performance modeling
- Familiarity with containers, orchestration, and AI infrastructure stacks
- Experience applying machine learning techniques to systems optimization or performance analysis
As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth. Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future. We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget. Micron also provides benefit programs that help protect your income if you are unable to work due to illness or injury, and paid family leave. Additionally, Micron benefits include a robust paid time-off program and paid holidays. For additional information regarding the Benefit programs available, please see the Benefits Guide posted on micron.com/careers/benefits.
Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, citizenship status, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
To learn more about Micron, please visit micron.com/careers
For US Sites Only: To request assistance with the application process and/or for reasonable accommodations, please contact Micron’s People Organization at hrsupport_na@micron.com or 1-800-336-8918 (select option #3)
Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.
Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.
AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.
Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.
See all 124+ OPT Performance Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Performance Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as a Performance Engineer
Target STEM-designated employers early
Companies with established STEM OPT extension programs already understand the paperwork and timelines. Prioritize employers who have filed H-1B petitions for performance engineers in previous years, which signals a real sponsorship pipeline for this role.
Frame your degree as directly relevant
Performance engineering qualifies as a specialty occupation under USCIS rules when tied to a technical degree. Emphasize your computer science, electrical engineering, or systems engineering coursework when applying, since degree-to-role alignment matters for both OPT authorization and future H-1B petitions.
Highlight measurable system improvements
Employers sponsoring OPT students need to justify complexity. Quantify your impact: latency reductions, throughput gains, and infrastructure cost savings demonstrate the specialized expertise that supports visa sponsorship and makes your application stand out against non-OPT candidates.
Apply before your OPT start date when possible
Many employers want immediate availability. Applying three to four months before your OPT start date gives hiring managers time to move through interviews and onboarding without gaps, reducing anxiety around your authorization timeline and increasing your odds of receiving an offer.
Ask about STEM extension policies during interviews
Not all employers are familiar with the 24-month STEM OPT extension. Asking directly during late-stage interviews signals you have a plan and helps you identify employers equipped to support you through the extension process before H-1B sponsorship becomes relevant.
Build expertise in high-demand performance tooling
Proficiency in tools like Grafana, Prometheus, JMeter, or distributed tracing frameworks makes you harder to replace and easier to sponsor. Employers are more likely to invest in H-1B sponsorship for engineers with specialized technical skills that are difficult to source domestically.
Performance Engineer OPT: Frequently Asked Questions
Does a Performance Engineer role qualify for the 24-month STEM OPT extension?
Yes, in most cases. Performance Engineer positions typically qualify for the STEM OPT extension because they fall under CIP codes related to computer science, computer engineering, or electrical engineering. Your degree field must be on the STEM Designated Degree Program List, and your employer must be enrolled in E-Verify. Confirm your specific degree code with your DSO before applying for the extension.
How do I find Performance Engineer jobs that explicitly sponsor OPT students?
Browse Performance Engineer listings on Migrate Mate, which filters jobs specifically for F-1 OPT and visa-sponsored candidates. Many general job boards do not distinguish between sponsors and non-sponsors, which wastes time. Migrate Mate surfaces employers who have demonstrated willingness to work with international candidates, saving you from applying to roles that will reject you at the offer stage.
Will employers see my OPT status as a risk when hiring for Performance Engineer roles?
Some will, but performance engineering is a technical specialty where qualified candidates are scarce. Employers in cloud infrastructure, enterprise software, and financial technology regularly hire OPT students because the talent pool is limited. Framing your STEM OPT timeline clearly, including the extension and eventual H-1B eligibility, helps employers plan around your authorization rather than treating it as an obstacle.
What happens to my Performance Engineer job if my H-1B is not selected in the lottery?
If you are not selected in the H-1B lottery, your OPT or STEM OPT authorization continues until its expiration date. Some employers will retain you through a second lottery attempt the following April. Others may explore alternatives like the O-1A visa for demonstrated expertise or, if applicable, the E-3 visa for Australian nationals. Discuss contingency plans with your employer before your STEM OPT expires.
Is performance engineering considered a specialty occupation for H-1B purposes?
Generally yes. USCIS evaluates specialty occupation status based on whether the role normally requires a bachelor's degree or higher in a specific technical field. Performance engineering roles requiring expertise in systems design, distributed computing, or load testing typically meet this standard. Job descriptions that clearly specify a degree requirement in computer science or engineering strengthen the H-1B petition and reduce the risk of a Request for Evidence.