Modem Machine Learning Engineer

Qualcomm

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$104,000–$156,000 / yr
Posted
9 days ago
Closes
Nov 30, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $219k
This role $130k
$85k most similar roles pay here $281k

This role pays less than 98% of similar roles. Most pay $187,464–$249,750 — the shaded band above. At the midpoint, this role pays about $130k versus about $219k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 660 open roles on FindRole.

Listed pay typically runs $154,000–$231,000 across 429 roles with salary data.

Most-posted roles

View all roles at Qualcomm

At a glance

TL;DR · Modem Machine Learning Engineer

As a Modem Machine Learning Engineer at Qualcomm Technologies, Inc., you will join the Engineering Group's Modem Technologies Software team to develop advanced machine learning solutions for next-generation modem systems. Your daily tasks include identifying high-impact ML use cases, designing and training robust models using deep learning architectures like CNNs and RNNs, and building scalable MLOps frameworks with tools such as AWS S3, Glue, EMR, Docker, Kubernetes, and Prometheus/Grafana. You will work on optimizing ML models for strict latency and memory constraints in HW-integrated environments, ensuring continuous performance monitoring and automated detection of data drift. The role requires strong programming skills in Python or C/C++, expertise in PyTorch or TensorFlow, and experience with large-scale datasets and production-grade ML pipelines.

What you'll do

  • Identify and prioritize high-impact machine learning use cases for modem systems.
  • Develop robust ML/DL models using advanced deep learning architectures for modem applications.
  • Build automated end-to-end ML pipelines for data ingestion to deployment.
  • Design state-of-the-art MLOps infrastructure for reproducible experimentation and model versioning.
  • Optimize ML models for on-device deployments with strict latency, memory constraints.
  • Implement robust monitoring systems for continuous tracking of model performance.

What we're looking for

  • Strong hands-on experience in Python or C/C++ programming.
  • Solid understanding of machine learning algorithms, probability, statistics, and software engineering principles.
  • Experience with deep learning architectures like CNNs, RNNs, GRUs, LSTMs, Transformers.
  • Proficiency in industry-standard ML frameworks such as PyTorch, TensorFlow, Keras.
  • Deep experience with MLOps systems including experiment tracking and model lifecycle management.
  • Ability to build production-grade ML pipelines for large-scale datasets.
  • Strong software engineering skills, including debugging complex integrated systems.

More like this

Similar roles

Modem Machine Learning Engineer

Qualcomm

San Diego, CA 17 days ago $104,000$156,000
Python PyTorch TensorFlow Keras AWS S3 Glue EMR Docker Kubernetes Apache Spark Databricks Delta Lake Postgres Prometheus Grafana CI/CD MLOps Kafka RabbitMQ

Machine Learning Engineer

Motorola Solutions

Los Angeles, CA 60 days ago $120,000$160,000
Python TensorFlow PyTorch scikit-learn MATLAB C++ signal processing wireless communication MIMO OFDM SDRs GPU acceleration embedded machine learning real-time systems adaptive modulation beamforming cognitive radio techniques 3GPP IEEE 802.11/15 military waveforms
Hybrid

Machine Learning Engineer

Qualcomm

San Diego, CA 37 days ago $122,800$184,200
Python TensorFlow PyTorch Keras C++ Linux Android NLP Machine Learning Embedded Systems Statistics Probability CI/CD

Machine Learning Engineer

Adobe

San Jose 78 days ago $183,300$265,350
Python PyTorch LangChain LangGraph MCP ADK LLMs VLLMs CI/CD Docker AWS PostgreSQL Kubernetes

Machine Learning Engineer

Adobe

San Jose 88 days ago $161,700$234,150
Python TensorFlow PyTorch scikit-learn SparkML Kubernetes AWS CI/CD SQL Docker PostgreSQL MLOps

Machine Learning Engineer

Q2

Austin, TX 50 days ago
Python TensorFlow PyTorch scikit-learn R Java cloud platforms scalable computing resources machine learning pipelines data analysis statistics optimization probability theory experimental methodologies CI/CD
Hybrid