Modem Machine Learning Engineer
$135,400 - $203,200 USD yearly
Originally posted 3 November 2026 by the employer.
About the role
The Modem Machine Learning Engineer applies machine learning techniques to modem systems, working across data engineering, model development, deployment, and lifecycle management.
What you'll do
- Identify, scope, and prioritize machine learning use cases within modem and wireless systems.
- Design, develop, and train ML and DL models tailored for modem applications, leveraging time-series forecasting, sequence modeling, and deep learning architectures.
- Build and integrate automated, end-to-end ML pipelines covering data ingestion, feature generation, model training, evaluation, and deployment.
- Design and maintain MLOps infrastructure for reproducible experimentation, model versioning, automation, and onboarding new ML use cases.
- Deploy and optimize ML models for on-device and modem targets, focusing on hardware and firmware integrated environments with strict latency, memory, and compute constraints.
- Implement model performance monitoring, establishing KPI regression tracking and automated detection for data and concept drift across cloud and on-target deployments.
What you'll need
- 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 (or Master's degree and 1+ year experience, or PhD).
- 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 have
- Hands-on experience with deep learning architectures including CNNs, RNNs, GRUs, LSTMs, Transformers, and related sequence models.
- Proficiency with ML frameworks such as PyTorch, TensorFlow, and Keras.
- Experience with MLOps systems, including experiment tracking, model lifecycle management, CI/CD for ML, and cloud/on-device co-development environments.
- Exposure to on-device ML deployment, quantization, and neural network optimization tools.
- Familiarity with cloud ML platforms (such as AWS SageMaker), containerization (Docker, Kubernetes), and automation or orchestration tools.
- Experience implementing data drift, concept drift, and model performance monitoring using KPIs in a production setting.
Skills: machine learning, modem, MLOps, Python, on-device
More semiconductor jobs: Qualcomm jobs · Software engineer jobs · San Diego jobs
This role has been open 0 days — well below the 81-day median for Software Engineering Semiconductor roles.
Software Engineering Semiconductor · AI ML Hardware
|
Open roles in category
151
|
Median days open
81 d
|
Category median salary (all employers)
$194k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Qualcomm | All employers we track in this specialty (151 roles · 42 employers) |
|---|---|---|
| Open roles in this specialty | 17 | 151 |
| Open roles in the wider Software, Firmware & Systems family | 681 | 6437 · 156 employers |
| Median days open | 209 d | 81 d (+128 d vs this employer) |
| Median salary (USD postings) | — | $194k |
Skills observed across this category: machine learning, modem, MLOps, Python, on-device
Who's hiring in this category
- NVIDIA · 34 open roles · median 233 d
- Micron Technology · 22 open roles · median 54 d
- Qualcomm (this employer) · 16 open roles · median 209 d
- Infineon Technologies · 12 open roles · median 54 d
- AMD · 8 open roles · median 71 d
- Applied Materials · 3 open roles · median 145 d
How we counted: 151 open Software Engineering Semiconductor (AI ML Hardware) roles from 42 employers tracked in the SemiconductorJobs index, counted 4 Nov 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.