Senior Machine Learning Engineer

• 6+ years experience as a machine learning engineer

• Expert knowledge in Python and PyTorch or TensorFlow

• Strong foundation in supervised and unsupervised learning and statistical modeling

• Strong mathematics background, particularly in linear algebra and probability

• Strong written and oral communication skills

Desired Skills

• Advanced degree in a quantitative field such as electrical and computer engineering, physics, mathematics or statistics
• Familiarity with relational and NoSQL databases
• Familiarity with RF signal processing and SDR for signals intelligence or electronic warfare
• Familiarity with cloud-based infrastructure: Azure and/or AWS
• Experience tracking projects with Jira, Azure DevOps or similar tooling
• Experience with Linux, DevOps (command line)
• Experience with containerized infrastructure (Docker, Kubernetes)
• Familiarity with regulated environments, such as sovereign clouds, * Bachelor degree with 8+ years experience as a machine learning engineer
• AND 6+ years of Python and PyTorch or TensorFlow experience
• Must be a U.S. citizen with the ability to obtain necessary security clearance as required by government contract.

• Incentive Bonus Plans
• Medical, Dental, Visionbenefits
• 401K with Company Match
• 10 Paid Holidays
• GenerousPaidTime Off Packages
• Employee Stock Purchase Plan
• PaidParental & Family Leave
• and more!

At Motorola Solutions, we believe that everything starts with our people. We're a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that's critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future. Department OverviewSilvus Technologies is dedicated to one mission: connecting those who keep us safe. We do so by delivering the most advanced Mobile Ad-hoc Network (MANET) radios powered by our custom and ever evolving Mobile-Networked MIMO waveform. Together, our radios and waveform provide vital communications for mission-critical applications in the harshest environments from underground tunnels to high-altitude balloons. Silvus StreamCaster radios are being rapidly adopted by customers all over the world ranging from the U.S. and Allied Nations Departments of Defense to International, Federal, State, and Local Law Enforcement agencies, all the way to the Superbowl, Grammys, and industry-leading drone, robot, and unmanned systems manufacturers. We're excited about the work we are doing in AI. Founded on our many years of experience and knowledge, we know the mission-critical needs of our customers are unique and different from the consumer technologies that leverage AI today. Silvus Technologies is a wholly owned subsidiary of Motorola Solutions, Inc. Job Description Would you like to join a talented group of people, doing challenging work, with the mission of "Keeping Our Heroes Connected"? You will join the Signals and Systems group where you will be developing machine learning solutions that enhance performance in wireless communication systems. Your work will directly impact our customers in the form of products and services that make use of signal processing technology. Unlike similar roles in the industry, the result of your work has the potential to impact the entire lifecycle of the workflows impacting people's lives in moments that matter. Roles and Responsibilities * Manage inputs gathered from unusual sources, including captures from software defined radio (SDR) over a wide range of RF signals * Combine knowledge of signal processing, probability and statistics, machine learning, and modern methods of artificial intelligence to build large-scale and high-throughput systems handling vast quantities of data * Collaborate with UX designers, infrastructure engineers, and other research scientists to develop prototypes and integrate ML algorithms that work across a wide range of scales from resource-constrained edge compute to full-sized data centers