Software Gigs

As a Senior Systems Engineer for Data Center AI at Qualcomm, you will research, develop, optimize, and validate AI/ML solutions that integrate Qualcomm's hardware accelerators with software and ecosystem to deliver high-performance inference in datacenter environments. You will design and deploy Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking, and drive system-level architecture decisions. You will collaborate across functional teams to meet system-level requirements while staying current with advances in AI/ML models and hardware. Required skills include strong Python proficiency, expertise in ML frameworks (PyTorch, TensorFlow), deep understanding of ML deployment and system performance profiling, and experience with parallel computing.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams. The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

This intermediate-level role develops AI-enhanced decision systems for government sponsors, applying machine learning, large language models, and decision science to mission-critical problems. You will design, prototype, and evaluate AI solutions in cross-functional teams, lead R&D initiatives, and work hands-on with modern AI techniques including deep learning, reinforcement learning, generative AI, and multi-agent systems. The role requires 2+ years of hands-on ML experience (or 1 year with a Master's degree) and proficiency in Python and/or C++, with the ability to communicate complex technical concepts to both technical and non-technical audiences.