Own customer AI engagements as technical lead: translate customer requirements into deployment-ready serving architectures, drive model optimization and validate production readiness
Drive development and deployment of GenAI and LLM applications, including architecture decisions for fine-tuning, quantization, speculative decoding and disaggregated serving
Define and execute benchmarking programs across the hardware and software stack; perform root-cause analysis on complex system-level issues and communicate findings to engineering and customer stakeholders
Mentor and coach engineers on technical approaches, debugging methodology and best practices; provide leadership on project execution and set clear expectations across contributors
Identify and prototype solutions to unmet technical challenges; challenge ingrained practices by introducing approaches drawn from current research and validated against production hardware
Collaborate across compiler, runtime, serving, model engineering and customer engineering teams to resolve cross-functional blockers and deliver system-level objectives
Define reusable platform capabilities, tooling and documentation that scale team capacity beyond individual engagements
Contribute to strategic decisions on model portfolio prioritization, inference stack investment and customer deployment architecture
Track advancements in AI and ML research and translate techniques relevant to Qualcomm's hardware capabilities into actionable product and solution improvements
Required Skills
Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow)
Deep understanding of ML model development, deployment and production inference
Deep understanding of system performance profiling and parallel computing
Preferred Qualifications
Master's or PhD in Engineering, Information Systems, Computer Science, Physics or related field
Advanced understanding of GenAI architectures including transformers, diffusion models, LLMs, large vision-language models and embedding models
Hands-on experience with inference optimization techniques: speculative decoding, KV cache management, continuous batching and tensor/pipeline parallelism
Experience fine-tuning and distilling GenAI models at scale; familiarity with RLHF and RLAIF methods is a plus
Background in compiler optimizations for ML workloads targeting heterogeneous hardware
Experience designing and operating large-scale distributed AI systems with knowledge of microservice and event-driven architectures
Proficiency with MLOps practices and ML lifecycle tooling; experience with containerization and automation (Docker, Kubernetes, GitOps) is expected
Experience with observability and performance analysis for ML pipelines: metrics collection, distributed tracing and capacity modeling
Track record of leading complex technical programs across large, matrixed organizations
Experience with open-source development workflows and version control systems (Git, GitHub, GitLab, Gerrit)
Experience with rack-level orchestration and datacenter automation is a plus
Minimum Qualifications
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
- OR
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
- OR
- PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
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