Senior Engineer - Machine Learning

Qualcomm•San Diego, CA

41d•$140,800 - $211,200

About The Position

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.

Requirements

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.
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.
PhD in Computer Science, Engineering, Information Systems, or related field.

Nice To Haves

Strong 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

Responsibilities

Design and implement machine learning models and pipelines for production use
Build scalable training → evaluation → deployment workflows
Develop reusable ML components, libraries, and frameworks
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
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
Design data pipelines for ingestion, preprocessing, feature engineering, and validation
Improve data quality and model reliability through systematic evaluation
Partner with product, platform, and hardware teams to deliver end-to-end ML solutions
Participate in design reviews and contribute to system architecture decisions