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Lead Engineer, Senior-System Performance Engineer (NSP, AI/ML, CPU, DDR)

Qualcomm · Posted Oct 26

Semiconductor, 5G wireless technology, mobile platform, and AI computing solutions

Bangalore, IndiaFull-timeOnsiteLead/Staff5+ years₹35.0L–₹55.0L yearly44 applicants
SemiconductorsWireless TechnologyTelecommunicationsArtificial IntelligenceMobile ComputingPublic Company
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About the role

The System Performance team at Qualcomm is responsible for profiling and optimizing system performance on Snapdragon data center chipsets. The role sits in systems engineering and requires strong knowledge of AI/ML performance using NPU cores, with CPU, DDR, and NoC knowledge as an added advantage. The engineer will drive silicon performance analysis with benchmarks, use profiling tools to find system bottlenecks, and work with cross-functional global teams on recommendations for next-generation chipsets.

What you will do

  • Drive Performance analysis on silicon using various System and Cores (i.e. NPU, AI/ML, CPU, Memory) benchmarks like Dhrystone, GeekBench, SPECInt, CNN/GenAI ML networks etc.
  • Use of Performance tools to analyze the load patterns across IPs and identify any performance bottlenecks in system.
  • Analyzing Perf KPIs of SoC subsystems like NPU, CPU, Memory, and corelate performance with projection
  • Evaluate and characterize performance at various junction temperatures and optimize running at high ambient temperatures.
  • Analyze and optimize the System performance parameters of SoC infrastructure like NoC, LP5 DDR, etc.
  • Collaborate with cross-functional global teams to plan and execute performance activities on chipsets as well as make recommendations for next generation chipsets.

Skills used in this role

NPUCPUDDRLPDDR5AI/MLTensorFlow LitePyTorch MobileONNX RuntimeQNNOpenVINOLinuxQNXARMx86Intel VTuneLinux perfSnapdragon ProfilerWindows Performance ToolkitGeekbenchSPECintCoreMarkHypervisorComputer ArchitectureNoCSoC

What the employer is looking for

  • A deep understanding of NPU, CPU and DDR architecture internals like
  • NPU: Understanding of NPU micro-architecture, including its specialized processing elements for operations like matrix multiplications and convolutions.
  • A strong grasp of deep learning fundamentals, including neural network architectures like Convolutional Neural Networks (CNNs), transformers, GenAI etc.
  • Understanding the distinction between the compute-intensive training of a model (often done on GPUs) and running inference (making predictions) on NPUs.
  • Knowledge of Model optimization and compression techniques (Quantization, Pruning, Distillation etc.)
  • CPU caches (L1, L2, L3), instruction pipelines, branch prediction, memory hierarchy (including register, cache, and main memory) and multi-core/multi-threaded processing. You need to understand how memory access, instruction dependencies, and contention for shared resources can impact performance.
  • DDR: Understanding of JEDEC specifications LPDDR4, LPDDR5, LPDDR6, including command and timing parameters (e.g., 𝑡𝐶𝐿, 𝑡𝑅𝐶𝐷, tRP), memory organization (rows, columns, banks), and basic view of training and initialization sequences, how a memory controller works and its specific features like command queue, port arbitration, and various control schemes.
  • Familiarity with frameworks like TensorFlow Lite, PyTorch Mobile, and ONNX Runtime for preparing models for deployment. Experience with NPU-specific compilers, such as those from Qualcomm (QNN), Arm (Vela), Intel (OpenVINO), to optimize and orchestrate AI workloads.
  • Evaluating hardware and software to determine the best fit for specific AI tasks and measure performance metrics like TOPS/W (trillions of operations per second per watt).
  • Using tools like the Windows Performance Toolkit and Qualcomm's Snapdragon Profiler to analyze and optimize NPU and system-level performance
  • A core understanding of how to use parallel processing architectures for efficient AI computations.
  • Expertise in how operating systems manage processes, threads, memory, MMU, and interrupt handling. This knowledge is crucial to understanding software for the kernel scheduler and system-level bottlenecks.
  • Good understanding of Benchmarks CPU (like GeekBench, SpecInt, CoreMark etc) and DDR (like lat_mem, stream, bw_mem etc.) and how they exercise the underlying CPU/GPU/DDR architecture.
  • Experience with a variety of performance monitoring tools like Intel VTune, Linux perf, and Utilities like top, vmstat, iostat, and netstat to monitor system resources like CPU, memory, and I/O. Experience with software tools to monitor system hotspots, command bus utilization, and identify memory traffic patterns is critical. This includes validating that the traffic generated by software is as expected.
  • Good understanding of memory allocation policies, prefetching, and caching to minimize latency and maximize bandwidth. Understanding how an application accesses memory is vital. Skills in profiling code to analyze memory access patterns and then optimizing the code for better data locality.
  • Analyzing large datasets from performance tests requires strong statistical skills. This can involve creating histograms of transaction latencies and deriving performance metrics to understand a system's behavior.
  • 5+ years of industry experience in the following:
  • Experience working on any ARM/x86 based platforms, mobile/automotive/Data center operating systems and/or performance profiling tools.
  • Experience in application or driver development in Linux\QNX and ability to create/customize make files with various compiler options is a plus.
  • Must be quick learner and should be able to adapt to new technologies.
  • Must have excellent communication skills.
  • Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
  • Required: Bachelor's, Computer Engineering, and/or Electrical Engineering

Preferred qualifications

  • Knowledge of Computer architecture, LP5 DDR, Bus/NOC profiling is a big plus.
  • Fundamentals on any operating system like Linux/QNX/Hypervisor & experience working on any Data center applications.
  • Experience in creating professional quality reports and slides using MS Office or any advanced visualization tools.
  • Experience in PoC development and competitive analysis Knowledge on Voltage/Power/ Thermal domain is plus.
  • Preferred: Master's, Computer Engineering, and/or Electrical Engineering

Benefits and support

  • Compensation and benefits are detailed in the job posting

About Qualcomm

Qualcomm logo

Qualcomm

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Qualcomm is a global leader in the development and commercialization of foundational technologies for the wireless industry, including mobile processors, 5G, and on-device artificial intelligence. The company designs and markets digital communications products, semiconductor solutions, and system software used globally across smartphones, automotive solutions, IoT devices, and computing systems. Through extensive R&D and intellectual property licensing, Qualcomm powers a highly interconnected and smarter world.

Industry
Semiconductors
Company size
50000+ employees
Founded
1985
Location
San Diego, California, USA
Funding stage
Public Company

Funding

Public Company · $53M raised

Western Technology Investment
  • 2018-11-01Grant

Leadership

CR
Cristiano R. Amon

President and Chief Executive Officer

AP
Akash Palkhiwala

Chief Financial Officer and Chief Operating Officer

JH

James H. Thompson

Chief Technology Officer

DM
Don McGuire

Chief Marketing Officer

AC

Ann Chaplin

General Counsel and Corporate Secretary

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