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About the Role

Qualcomm envisions making AI ubiquitous, expanding beyond mobile and powering other end devices, data centers, vehicles, and things. We are inventing, developing, and commercializing power-efficient on-device AI, edge cloud AI, data center and 5G to make this a reality. We are looking for AI Accelerator Architecture Engineers to drive functional, performance and power enhancements into the HW to enable state-of-the-art training capabilities. AI inference and training systems must scale to a large number of accelerators, servers and racks. Our devices must be designed to scale to handle the largest of today's models. The AI Architecture team is comprised of experts that span the full gamut from software architecture, algorithm development, kernel optimization, down to hardware accelerator block architecture and SOC design. The ideal candidate will augment the team by contributing to one or many of these areas.

Responsibilities

Understand trends in ML network design through customer engagements and latest academic research and determine how this will affect both SW and HW design
Work with customers to determine hardware requirements for AI training systems
Analysis of current accelerator and GPU architectures
Architect enhancements required for efficient training of AI models
Design and architecture of flexible computational blocks involving a variety of datatypes and precision, capable of optimally performing dense and sparse GEMM, GEMV
Design and architecture of memory technology and subsystems optimized for a range of requirements (capacity, bandwidth, compute in memory, compute near memory)
Design and architecture of scale-out and scale-up architectures, switches, NoCs, codesign with communication collectives
Ability to perform competitive analysis
Codesign HW with SW/GenAI (LLM) requirements
Define performance models to prove effectiveness of architecture proposals
Pre-Silicon prediction of performance for various ML training workloads
Perform analysis of performance/area/power trade-offs for future HW and SW ML algorithms including impact of SOC components (memory and bus impacts)

Requirements

Master's degree in Computer Science, Engineering, Information Systems, or related field
3+ years Hardware Engineering experience defining architecture of GPUs or accelerators used for training of AI models
In-depth knowledge of nVidia/AMD GPU capabilities and architectures
Knowledge of LLM architectures and their HW requirements
Knowledge of computer architecture, digital circuits and hardware simulators
Knowledge of communication protocols used in AI systems
Knowledge of Network-on-Chip (NoC) designs used in System-on-Chip (SoC) designs
Understanding of various memory technologies used in AI systems
Experience in modeling hardware and workloads in order to extract performance and power estimates
High-level hardware modeling experience preferred
Knowledge of AI Training systems such as NVIDIA DGX and NVL72
Experience training and finetuning LLMs using distributed training framework such as DeepSpeed, FSDP
Knowledge of front-end ML frameworks (i.e., TensorFlow, PyTorch) used for training of ML models
Strong communication skills (written and verbal)
Detail-oriented with strong problem-solving, analytical and debugging skills
Demonstrated ability to learn, think and adapt in a fast-changing environment
Ability to code in C++ and Python
Knowledge of a variety of classes of ML models (i.e. CNN, RNN, etc)
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Science, Engineering, Information Systems, or related field.

Skills

Machine Learning
Hardware Architecture
Software Architecture
Algorithm Development
Kernel Optimization
SOC Design
Floating Point
Fixed Point
Microscaling
32/16/8/4/2/1 Precision
GEMM
GEMV
Memory Technology
Compute in Memory
Compute near Memory
Scale-Out Architectures
Scale-Up Architectures
NoC
Communication Collectives
Power Optimization
Competitive Analysis
GenAI
LLM
Performance Modeling
Pre-Silicon Performance Prediction
Performance/Area/Power Trade-offs
ML Algorithms
SOC Components
nVidia GPU
AMD GPU
LLM Architectures
Computer Architecture
Digital Circuits
Hardware Simulators
Communication Protocols
Network-on-Chip (NoC)
System-on-Chip (SoC)
Memory Technologies
Hardware Modeling
NVIDIA DGX
NVL72
DeepSpeed
FSDP
TensorFlow
PyTorch
C++
Python
CNN
RNN

Location

Remote

Work Type

Full-time

Experience Level

3+ years Hardware Engineering experience
2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience
1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience

Education Level

Master's degree in Computer Science, Engineering, Information Systems, or related field
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field
PhD in Computer Science, Engineering, Information Systems, or related field

Salary/Compensations

$126,700.00 - $190,100.00

Benefits

Competitive annual discretionary bonus program
Opportunity for annual RSU grants
Highly competitive benefits package designed to support your success at work, at home, and at play

About the Company

Qualcomm Technologies, Inc. is a company focused on making AI ubiquitous, expanding beyond mobile and powering other end devices, data centers, vehicles, and things. They are inventing, developing, and commercializing power-efficient on-device AI, edge cloud AI, data center and 5G.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

Equal Opportunity

Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities.
To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.