General Summary:We are seeking a highly skilled
Core ML Engineer to design, develop, and optimize machine learning systems that power next-generation AI platforms and applications. This role focuses on
model development, inference optimization, and scalable ML infrastructure, enabling production-grade AI capabilities across enterprise systems.
The ideal candidate combines
strong software engineering fundamentals with deep ML expertise, and thrives in building robust, high-performance systems at scale.
Key ResponsibilitiesCore ML System Development- Design and implement machine learning models and pipelines for production use
- Build scalable training evaluation deployment workflows
- Develop reusable ML components, libraries, and frameworks
Inference & Performance Optimization- Optimize model inference for latency, throughput, and cost
- Implement advanced techniques such as caching, quantization, batching, and routing
- Benchmark and profile models across diverse workloads and hardware environments
Model Integration & Deployment- Integrate ML/LLM models into APIs, microservices, and applications
- Build and maintain model-serving infrastructure (e.g., vLLM, ONNX, custom runtimes)
- Collaborate with platform and infrastructure teams for scalable deployment
Data & Pipeline Engineering- Design data pipelines for ingestion, preprocessing, feature engineering, and validation
- Improve data quality and model reliability through systematic evaluation
Cross-functional Collaboration- Partner with product, platform, and hardware teams to deliver end-to-end ML solutions
- Participate in design reviews and contribute to system architecture decisions
Minimum Qualifications:• Bachelors 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
Masters 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.
Preferred QualificationsStrong programming skills in Python and at least one systems language (C++/Rust/Go)Solid understanding of:- Machine learning fundamentals (supervised, unsupervised, deep learning)
- Transformer architectures / LLMs
- Model evaluation and debugging
Experience with:- ML frameworks (PyTorch, TensorFlow)
- Model deployment and serving systems
- Building scalable software and APIs
Experience with:- Large Language Models (LLMs), multimodal models, or generative AI
- Retrieval systems and RAG pipelines
- Distributed computing and GPU/accelerator environments including model serving and efficient cache/state management (e.g. KV cache, embeddings) across disaggregated systems
- Kubernetes, Docker, and CI/CD pipelines
- Agentic and multi-step AI workflows, tool integration, orchestration, and multi-component pipelines
Knowledge of:- Model optimization techniques (quantization, distillation, caching)
- Vector databases and search systems (OpenSearch, Qdrant, etc.)
- Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies
Pay range and Other Compensation & Benefits: $140,800.00 - $211,200.00
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.
