Modem Machine Learning Engineer

Qualcomm•San Diego, CA

2d•$128,000 - $192,000

About The Position

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.

Requirements

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.
Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 1+ year of Software Engineering, Electrical Engineering, or related work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field.
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.

Nice To Haves

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, Keras.
Proven experience building production‑grade ML pipelines capable of handling large‑scale structured and unstructured datasets.
Deep experience with MLOps systems, including experiment tracking, model lifecycle management, CI/CD for ML, and cloud/on‑device co‑development environments.
Experience implementing data drift, concept drift, and model performance monitoring using well‑defined KPIs in a production setting.
Strong software engineering skills, including object‑oriented design, debugging complex integrated systems, and working within real‑time execution constraints.
Exposure to on‑device ML deployment, quantization, and neural network optimization tools.
Familiarity with cloud ML platforms (e.g., AWS SageMaker), containerization (Docker/Kubernetes), and automation/orchestration tools.

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).