ML Engineer - Wireless RF Systems (On-site LA)

Silvus Technologies; Los Angeles (CA); On-site; USD 150,000 - 180,000; Full time

Benefits offered by this job

Medical, Dental, Vision benefits

401K with Company Match

Incentive Bonus Plans

Paid Holidays

Job summary

Silvus Technologies in Los Angeles is seeking an experienced machine learning engineer to join the Signals and Systems group. You will develop ML solutions for wireless communications, impacting products and services used in mission-critical applications.

The role emphasizes collaboration across engineering and research teams, delivering prototypes and scalable ML workflows, with a strong focus on RF signal processing and embedded systems.

Qualifications

• Bachelor degree in a related field.
• 4+ years of experience as a machine learning engineer.
• 2+ years of Python and PyTorch or TensorFlow experience.
• Experience with RF signal processing.
• Strong foundation in statistical modeling and mathematics.
• Excellent written and oral communication skills.

Responsibilities

• Manage inputs from SDR signals over a wide RF range.
• Develop ML algorithms for wireless systems at scale.
• Collaborate with UX, infra engineers, and research scientists to prototype and deploy.
• Stay up-to-date with ML research for wireless and embedded systems.

Skills

Machine learning engineer

Python & ML frameworks

RF signal processing

Supervised & unsupervised learning

Mathematics (linear algebra, prob.)

Communication skills

Education

Advanced degree in quantitative field

Bachelor degree

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Jira

Azure DevOps

Linux

AWS

Azure

Job description

Silvus Technologies in Los Angeles is seeking an experienced machine learning engineer to join the Signals and Systems group. You will develop ML solutions for wireless communications, impacting products and services used in mission-critical applications.

The role emphasizes collaboration across engineering and research teams, delivering prototypes and scalable ML workflows, with a strong focus on RF signal processing and embedded systems.