Senior/Staff Software Engineer - LLM Inference Systems & Open-Source

QUALCOMM, Inc.; Santa Clara (CA); On-site; USD 162,000 - 243,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking experienced software engineers to advance the software ecosystem for large-scale LLM inference on Qualcomm platforms.

You will work at the intersection of open-source LLM serving systems, distributed systems, and cloud-native infrastructure, contributing to llm-d and related engines to run efficiently on Qualcomm accelerators. You may focus on distributed LLM serving or inference runtime and accelerator integration while collaborating across both areas.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ 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 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Strong software engineering and debugging skills.
• Experience developing production-quality software in one or more of Python, C++, Go, or Rust.
• Strong understanding of ML/inference systems, distributed systems, AI accelerator software, cloud-native/Kubernetes infrastructure, ML compiler/runtime systems.

Responsibilities

• Contribute features, improvements, bug fixes, and architectural enhancements to open-source LLM inference projects, including llm-d and other open-source LLM inference and serving projects.
• Develop and improve support for Qualcomm AI accelerators in open-source LLM serving frameworks.
• Work with upstream maintainers and open-source communities to design, review, and upstream Qualcomm-related functionality.
• Develop and enhance inference engine integrations for Qualcomm platforms, including device abstraction, accelerator runtime integration, memory management, distributed execution, compilation, and performance optimization.
• Develop and improve llm-d integration and deployment capabilities for Qualcomm platforms, including reference architectures, deployment blueprints, configuration, and operational tooling.
• Design and implement distributed LLM inference capabilities such as prefill/decode disaggregation, KV-cache management, distributed communication, load balancing, model parallelism, multi-node and scale-out inference, autoscaling and resource management.
• Integrate LLM inference software with Kubernetes and cloud-native infrastructure, including scheduling, autoscaling, networking, observability, and lifecycle management.
• Profile, benchmark, and optimize end-to-end inference performance including TTFT, ITL, throughput, memory utilization, and scalability.

Skills

Python

C++

Go

Rust

Distributed systems

LLM inference

Cloud-native/Kubernetes

Education

Bachelor's degree in CS/Engineering

Master's degree in CS/Engineering

PhD in CS/Engineering

Tools

llm-d

KServe

Kubernetes

Torch

Job description

Company:

Qualcomm Technologies, Inc.

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

We are looking for experienced software engineers to help build and advance the software ecosystem for large-scale LLM inference on Qualcomm platforms.

In this role, you will work at the intersection of open-source LLM serving systems, distributed systems, cloud-native infrastructure, and AI accelerator software. You will contribute to open-source projects such as llm-d and related LLM inference engines, while developing the integrations and capabilities required to run these systems efficiently and reliably on Qualcomm AI accelerators.

Depending on your background and interests, you may focus more heavily on either distributed LLM serving and orchestration or inference runtime and accelerator integration, while collaborating across both areas.

This is an opportunity to work directly with leading open-source communities and help shape the next generation of scalable, production-grade LLM inference infrastructure.

Minimum Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ 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 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Responsibilities

• Contribute features, improvements, bug fixes, and architectural enhancements to open-source LLM inference projects, including llm-d and other open-source LLM inference and serving projects.
• Develop and improve support for Qualcomm AI accelerators in open-source LLM serving frameworks.
• Work with upstream maintainers and open-source communities to design, review, and upstream Qualcomm-related functionality.
• Develop and enhance inference engine integrations for Qualcomm platforms, including device abstraction, accelerator runtime integration, memory management, distributed execution, compilation, and performance optimization.
• Develop and improve llm-d integration and deployment capabilities for Qualcomm platforms, including reference architectures, deployment blueprints, configuration, and operational tooling.
• Design and implement distributed LLM inference capabilities such as:
  • Prefill/decode disaggregation
  • KV-cache management and transfer
  • Distributed communication and high-performance data movement
  • Request routing and load balancing
  • Tensor, pipeline, expert, and other forms of model parallelism
  • Multi-node and scale-out inference
  • Autoscaling and resource management
  • Failure detection, recovery, and service resiliency
• Integrate LLM inference software with Kubernetes and cloud-native infrastructure, including scheduling, autoscaling, networking, observability, and lifecycle management.
• Profile, benchmark, and optimize end-to-end inference performance, including time to first token (TTFT), inter-token latency (ITL), throughput, memory utilization, and scalability.
• Diagnose complex issues spanning model-serving frameworks, distributed runtimes, operating systems, networking, accelerator runtimes, and hardware.
• Collaborate closely with hardware, firmware, compiler, runtime, Kubernetes, and product teams to define requirements and deliver end-to-end solutions.
• Develop technical documentation, examples, reference deployments, and best practices to help customers and developers deploy LLM workloads on Qualcomm platforms.
• Participate in architecture discussions and help influence the technical direction of Qualcomm's open-source LLM inference software strategy.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
• Strong software engineering and debugging skills.
• Experience developing production-quality software in one or more of Python, C++, Go, or Rust.
• Strong understanding of at least one of the following areas:
  • Machine learning or LLM inference systems
  • Distributed systems
  • AI accelerator or GPU software
  • Cloud-native/Kubernetes infrastructure
  • ML compiler or runtime systems
• Experience working with complex software systems and debugging problems across multiple layers of the stack.
• Ability to collaborate effectively across teams and communicate technical concepts clearly.

Preferred Qualifications

• LLM inference engines, model-serving frameworks, or distributed inference systems.
• Architecture and implementation of high-performance LLM inference runtimes, including model execution, scheduling, memory management, attention, and KV-cache management.
• Distributed LLM inference architectures, including prefill/decode disaggregation and KV-cache transfer.
• llm-d, KServe, Kubernetes Gateway API, Kubernetes operators, or cloud-native AI infrastructure.
• PyTorch internals, torch.compile, Dynamo, Inductor, Triton, MLIR, or other compiler/runtime technologies.
• Hardware accelerator software stacks, GPU programming, or custom AI accelerator integration.
• RDMA, RoCE, GPU Direct-style communication, PCIe, high-speed networking, or distributed communication libraries.
• Model parallelism, including tensor parallelism, pipeline parallelism, expert parallelism, and data parallelism.
• Performance profiling and optimization of distributed AI workloads.
• Kubernetes scheduling, autoscaling, networking, observability, and production operations.
• Open-source development, including contributing patches, participating in design discussions, reviewing code, and collaborating with upstream maintainers.
• Experience working across hardware/software boundaries with hardware, firmware, compiler, runtime, and systems teams.

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Pay range and Other Compensation & Benefits:

$162,000.00 - $243,000.00

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.

If you would like more information about this role, please contact Qualcomm Careers.