Senior Engineer - Machine Learning

Qualcomm; San Diego (CA); On-site; USD 140,800 - 211,200; Full time

Benefits offered by this job

Competitive annual discretionary bonus program

Opportunity for annual RSU grants

Comprehensive benefits package

Job summary

Qualcomm is looking for a highly skilled Core ML Engineer based in San Diego, California, who will design, develop, and optimize machine learning systems for AI platforms. This role centers on creating scalable ML infrastructure and model optimization.

The ideal candidate should possess strong programming skills in Python, familiarity with machine learning fundamentals, and the ability to work on high-performance systems. The salary range for this role is $140,800.00 - $211,200.00, with additional bonus opportunities and a competitive benefits package.

Qualifications

• 2+ years of experience in Hardware, Software, or Systems Engineering.
• Strong programming skills in Python and one systems language.
• Solid understanding of machine learning fundamentals and Transformer architectures.

Responsibilities

• Design and implement ML models and pipelines for production.
• Optimize model inference for latency and throughput.
• Integrate ML models into APIs and microservices.

Skills

Python

C++

Rust

Machine learning fundamentals

Transformers

Model evaluation

ML frameworks (PyTorch, TensorFlow)

Kubernetes

Docker

CI/CD pipelines

Education

Bachelor's degree in Computer Science or related field

Master's degree in Computer Science or related field

PhD in Computer Science or related field

Job description

Company

Qualcomm Incorporated

Job Area

Engineering Group, Engineering Group > Machine Learning Engineering

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 Responsibilities

Core ML System Development

• Design and implement machine learning models and pipelines for production use.
• Build scalable training, evaluation, and 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

• 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.

Preferred Qualifications

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, model deployment, and building scalable software and APIs

• ML frameworks (PyTorch, TensorFlow).
• Model deployment and serving systems.
• Building scalable software and APIs.

Experience with LLMs, retrieval systems, and distributed compute

• 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. 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.

Equal Opportunity Statement

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. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.