Skip to main contentOur CompanyOur BusinessSearch for JobsJoin Talent NetworkFAQsProfileEnglishSign InWelcome to an enhanced Qualcomm Careers platform! We’ve reimagined our site to streamline your experience as you explore opportunities and invent the future with Qualcomm. Get started by creating a profile using sign in, setting your preferences, and managing your Qualcomm job search—all in one place. Note: Your past applications will be available when you sign in using the same email address you previously used to apply.Single PositionView All JobsModem Machine Learning EngineerSan Diego, California, United States of AmericaApply NowAdd to Job CartFind out how well you match with this jobUpload your resumeJob descriptionCompany and benefitsJob ID3090967Company:Qualcomm Technologies, Inc.Job Area:Engineering Group, Engineering Group > Modem Technologies SoftwareGeneral Summary:The Modem Machine Learning Engineer applies advanced machine learning techniques to next‑generation modem systems, working across data engineering, model development, deployment, and lifecycle management. This role partners closely with modem, systems, and software teams to deliver production‑ready ML solutions. You will place a strong emphasis on modern deep learning architectures, building scalable MLOps frameworks, and ensuring continuous model health monitoring in dynamic production environments. Key Responsibilities Identify, scope, and prioritize high-impact machine learning use cases within modem and wireless systems. Design, develop, and train robust ML/DL models tailored for modem applications, leveraging time‑series forecasting, sequence modeling, and modern deep learning architectures. Build and integrate automated, end‑to‑end ML pipelines encompassing data ingestion, feature generation, model training, evaluation, and deployment. Design and maintain state-of-the-art MLOps infrastructure to enable reproducible experimentation, strict model versioning, automation, and the scalable onboarding of new ML use cases. Deploy and heavily optimize ML models for on‑device and modem targets, specifically focusing on HW and firmware integrated environments with strict latency, memory, and compute constraints. Implement robust model performance monitoring, establishing KPI regression tracking and automated detection for data and concept drift across both cloud and on‑target deployments. Collaborate closely across systems, test, and platform teams to ensure a seamless production rollout and sustained model performance over time. Design and implement ETL, data platform, MLOps, CI/CD, observability, and governance pipelines across on-premises and cloud environments. Build and manage ML data platforms utilizing hands-on experience with AWS (S3, Glue, EMR), containers (Docker, Kubernetes), streaming/messaging (Kafka, RabbitMQ), data platforms (Spark, Databricks, Delta Lake/Iceberg/Hudi, SQL, Postgres), and observability stacks (Prometheus/Grafana, Datadog, Splunk). Minimum Qualifications:• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Software Engineering, Electrical Engineering, or related work experience. ORMaster's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 1+ year of Software Engineering, Electrical Engineering, or related work experience. ORPhD in Computer Engineering, Computer Science, Electrical Engineering, or related field.Minimum Qualifications Bachelor’s degree with at least 1 year of relevant experience or Master’s degree Strong hands-on programming experience in Python and/or C/C++. Solid foundations in machine learning algorithms, probability, statistics, and software engineering principles. Preferred Qualifications Hands‑on experience with deep learning architectures including CNNs, RNNs, GRUs, LSTMs, Transformers, and related sequence models. Proficiency with industry-standard ML frameworks such as PyTorch, TensorFlow,...
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