Jobs / Sam***
Senior Staff Engineer, GPU Architect, Machine Learning
Sam*** · San Jose, CA, United States
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San Jose, CA, United States198,200-297,200 USD/yearlyOnsite
Remuneration
198,200-297,200 USD/yearly
Location
San Jose, CA, United States
Visa sponsorship
Sponsors visa
Job summary
Position Summary Samsung, a world leader in advanced semiconductor technology, is founded on a simple philosophy – the endless pursuit of excellence will create a better world for all.
Benefits
Additionally, this role might be eligible to participate in long term incentiveThis is an exempt position, which is not eligible for overtime pay under the FaiExport ControlThis position requires the ability to access information subject to U.Export control restrictions.Trade SecretsSARC #ACLPlease visit Samsung membership to see Privacy Policy, which defaults accordingYou can change Country/Language at the bottom of the page.Samsung Electronics America, Inc.
Qualifications
- 11+ years of experience with a Bachelor's degree in Computer Science/Computer Engineering/relevant technical field, or 9+ years of experience with a Master's degree, or 7+ years of experience with a PhD.
- Knowledge of machine learning principles and architectures
- Knowledge of computer vision algorithms, AR/VR, and Ray Tracing
- Knowledge of computer architectures and performance optimization
- Experience with workload analysis and modeling
- Strong programming
- experience, and work location.
- Samsung employees have access to
Responsibilities
- As a Senior Staff GPU Architect – Machine Learning, you will help lead the design and development of innovative machine learning (ML) solutions for Samsung’s premium mobile GPUs.
- In this high-impact individual contributor role, you will drive architectural innovation at the intersection of GPU and machine learning design to enable efficient execution of large-scale AI models.
- You are passionate about architecting and defining ML-driven hardware features and extensions that integrate seamlessly with the graphics pipeline, advancing GPU performance, power, and functionality for AI workloads.
- You support design excellence by working on improving middleware and hardware-software integration to optimize GPU architectures for ML workloads—ensuring maximum performance and power efficiency.
- You take initiatives on moderate-to-complex projects and help advance best practices, methodologies by staying ahead of industry trends and emerging
Skills
Communication
Degrees
AssociateBachelorDegreeMasterPhD
Work schedule
Overtime
Industry
AutomotiveEducationEnergyInsurancePublic-sector
Company size
SmbStartup
Contract length
6 months
Security clearance
ConfidentialSecret
Relocation
Yes