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.
