Machine Learning Engineer

Motorola Solutions

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Los Angeles, CA
Salary
$150,000–$180,000 / yr
Employment
Full-time
Posted
49 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k
This role $165k
$135k $293k
below market most similar roles pay here above market

This role pays less than 89% of similar roles. Most pay $200,437–$251,925 — the blue band above. At the midpoint, this role pays about $165k versus about $226k for comparable roles.

Based on 240 similar postings.

Employer

About Motorola Solutions

Motorola Solutions, Inc. (NYSE: MSI) is a leading American technology company providing mission-critical communications, video security, and analytics for public safety and enterprise customers.

Motorola Solutions currently has 156 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 120 roles with salary data.

Most-posted roles

View all roles at Motorola Solutions

At a glance

TL;DR · Machine Learning Engineer

JOB TITLE: Machine Learning Engineer The Machine Learning Engineer joins the Signals and Systems group to develop machine learning solutions that enhance performance in wireless communication systems. This role involves managing inputs from unusual sources, including software defined radio captures over a wide range of RF signals. You will combine knowledge of signal processing, probability, statistics, and modern artificial intelligence methods to build large-scale, high-throughput systems handling vast quantities of data. Key responsibilities include collaborating with UX designers and infrastructure engineers to develop prototypes and integrate ML algorithms across scales from resource-constrained edge compute to full-sized data centers. Required technical skills include Python, PyTorch or TensorFlow, RF signal processing, and SDR for signals intelligence. You will apply a strong mathematics background in linear algebra and probability to solve complex wireless communication problems.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

What you'll do

  • Manage inputs gathered from unusual sources, including captures from software defined radio over a wide range of RF signals.
  • Build large-scale and high-throughput systems handling vast quantities of data using signal processing, probability, statistics, and machine learning.
  • Develop prototypes and integrate ML algorithms that work across scales from resource-constrained edge compute to full-sized data centers.
  • Apply machine learning research for wireless and embedded systems to enhance performance in wireless communication systems.
  • Develop machine learning solutions that directly impact products and services using signal processing technology.
  • Stay current with the latest machine learning research to solve complex problems in wireless and embedded systems.

What we're looking for

  • 4+ years experience as a machine learning engineer.
  • 2+ years of experience with Python and PyTorch or TensorFlow.
  • 2+ years of experience with RF signal processing.
  • Expert knowledge in Python and an ML framework such as PyTorch or TensorFlow.
  • Experience with RF signal processing and SDR for signals intelligence or electronic warfare.
  • Strong foundation in supervised and unsupervised learning and statistical modeling.
  • Strong mathematics background, particularly in linear algebra and probability.
  • Must be a U.S. citizen with the ability to obtain necessary security clearance.
  • Advanced degree in a quantitative field such as electrical and computer engineering, physics, mathematics or statistics (preferred).
  • Familiarity with relational and NoSQL databases (preferred).
  • Familiarity with cloud-based infrastructure: Azure and/or AWS (preferred).
  • Experience tracking projects with Jira, Azure DevOps or similar tooling (preferred).
  • Experience with Linux, DevOps (command line) (preferred).
  • Experience with containerized infrastructure (Docker, Kubernetes) (preferred).
  • Familiarity with regulated environments, such as sovereign clouds (preferred).

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