Qualcomm Machine Learning EngineerAs a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation AI experiences and drive agentic transformation, creating a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will develop and implement cutting-edge tools and solutions to enable state-of-the-art AI solutions across various technology verticals.All Qualcomm employees are expected to actively support diversity on their teams, and in the Company.This role is open to both San Diego, CA and Raleigh, NC and will be onsite full-time.Minimum Qualifications:Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field.What You'll DoModel Reauthoring & Architecture AdaptationReauthor generative AI architectures for efficient execution on Qualcomm AI hardware. This covers LLMs (Llama, Phi, Qwen) and multimodal models (vision-language, speech, diffusion), including custom attention, normalization, positional embedding, and modality-specific components.Translate hardware execution constraints — operator support, memory layout, dispatch behavior — into model-level transformations.
These transformations need to preserve accuracy while enabling efficient on-device execution.Build clean extension points so internal teams and external contributors can onboard new architectures without changing core pipeline code.Inference Optimization for Edge HardwareIntegrate inference acceleration techniques into the model preparation pipeline. This includes memory-efficient attention, decode acceleration, and serving-time optimizations.Translate end-customer deployment constraints — target SoC, context length, latency budget, memory envelope — into concrete model preparation strategies.Custom Model & OEM EnablementWork with research teams to develop reauthoring strategies for custom OEM models and customer-specific use cases. Take research prototypes and turn them into production deployments.Cross-Functional CollaborationPartner with compiler teams to understand on-target constraints. Decide on the right response: a graph-level optimization or model-level reauthoring.Partner with quantization engineers so architectural decisions compose cleanly with the quantization stack.Pipeline & ToolingContribute reauthoring and adaptation stages to a multi-stage model preparation pipeline. Build developer-facing diagnostics that give clear, actionable feedback when models fail to lower or run efficiently.Minimum QualificationsExperience in ML systems, model optimization, or inference engineering.
Proficient in Python in large, typed codebases.Strong written and verbal communication. Comfortable operating across AI compiler, AI research, and partner-facing teams.Preferred QualificationsDeep implementation-level knowledge of generative AI architectures across LLMs and multimodal modelsDemonstrated experience optimizing inference for edge or resource-constrained deployments, with measurable latency or memory wins to point to.Strong PyTorch internals knowledge — module customization, export flows, tracing. Familiarity with the HuggingFacetransformersecosystem.Familiarity with on-device runtimes and SoC-level constraints (memory bandwidth, compute precision, NPU/DSP execution). Exposure to QAIRT/QNN, ONNXRuntime, LiteRT-LLM or similar is a plus.Working understanding of how quantization interacts with model architecture decisions, even if you're not a quantization specialist.Experience using agentic coding tools such as GitHub Copilot, Cursor, Claude Code, Codeium, or similar AI-assisted development tools to improve coding productivity and problem-solvingLevel of ResponsibilityWorks independently on open-ended optimization challenges. Provides technical guidance and mentorship to teammates.Decisions have broad impact on model accuracy, on-device performance, and the developer experience of teams using the preparation pipeline.Communicates complex model architecture and inference optimization concepts to a range of audiences: hardware engineers, research scientists, compiler engineers, OEM partners, and external developers.Has meaningful influence on the generative AI optimization roadmap, supported model strategy, and cross-team integration priorities.Qualcomm is an equal opportunity employer.
If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities.Pay range and Other Compensation & Benefits :$140,800.00 - $211,200.00