Lead LLM inference optimization for Qualcomm Cloud AI accelerators, working with customers and internal teams to deploy and tune generative AI workloads.
Responsibilities
Deploy and optimize LLM and multimodal inference workloads on Qualcomm Cloud AI accelerator platforms
Profile and optimize prefill and decode performance metrics
Analyze model architecture and runtime bottlenecks across compute, accelerator memory, host memory, KV cache, interconnect, networking, and software stack
Optimize model execution using quantization, batching, continuous/dynamic batching, KV-cache management, paged attention, prompt/context-length tuning, and speculative decoding
Optimize distributed inference across tensor, pipeline, data, context, and expert parallelism
Requirements
Master’s degree or higher in Computer Science, Electrical/Computer Engineering, AI/ML, or related technical field
You will be part of the team that develops the Intrusion Prevention System to power the Palo Alto Networks’s next generation firewall. The Decoder team is responsible for decoding network protocols and inspecting application traffic on enterprise customers’ networks.
Compensation
$172,100.00 - $278,300.00/yr
Responsibilities
Research networking protocols and related application networking behavior.
Research file types and file properties.
Research and provide defense mechanisms against popular protocol/firewall evasions
Develop protocol and file decoders, and integrate the detection solutions with Palo Alto Networks products
Build common services to support various network security services.
Requirements
BS/MS in Computer Science or Electrical Engineering or equivalent Military experience
Define and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.
Benchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.
Analyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.
Lead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
Lead technical design, build, and demonstration of scalable AI training solutions on NVIDIA GPU hardware and software for enterprise customers.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software.
Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption.
Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines.
Build customer-specific proofs of concept that address critical technical and business needs, demonstrating improvements in training performance, reliability, and efficiency.
Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
Nice to have: reinforcement learning from human feedback
Senior Solutions Architect to design, build, and optimize AI training solutions using NVIDIA GPU hardware and software on cloud platforms.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software.
Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption.
Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines.
Build customer-specific proofs of concept that address critical technical and business needs, demonstrating improvements in training performance, reliability, and efficiency.
Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration.
Requirements
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience.
Position:
Senior Engineer (Level 1)
Job Description:
Technical Skill [Required]
Expert in C coding
Working knowledge of any one RTOS for example Micrium OS (uCOS), FreeRTOS
Working knowledge with SiLabs, STMicro, NXP controllers based on Cortex M3, M4, M0+ low power mode
Peripherals: I2C, SDIO, UART, SPI, Timers, DMA, BT, BLE
Must have working knowledge of memory optimization and low power
Good to have: Understanding of MISRA C guideline
Good to have knowledge of Radio sub GHz protocols
Soft Skills [Required]
Positive attitude
Good analytical skills
Hungry for growth
Problem solving
Quick learner
Location:
IN-MH-Pune, India-Magarpatta City-Unit A (eInfochips)
Time Type:
Full time
Job Category:
Engineering Services
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