At a glanceSummarised by Seekless from the posting.
Must have12
Bachelors Degree or Advanced Degree in Computer Science, Software Engineering, or a related engineering discipline
6+ years of relevant software engineering experience
3+ years of experience with an Advanced Degree
Equivalent work experience will be considered in lieu of a degree
Design, build, and scale core ML infrastructure across heterogeneous compute (CPU/GPU), distributed storage, and networking resources
Develop and maintain end-to-end CI/CD automation pipelines for continuous machine learning model training, evaluation, packaging, and edge/cloud deployment
Implement comprehensive monitoring and alerting solutions to track real-time model performance, detect data/concept drift, and ensure sustained inference accuracy
Automate the end-to-end model life cycle to streamline transitions from experimentation to high-availability production environments
Drive operational maturity and system reliability to increase deployment velocity and support rapidly expanding AI/ML workloads
Maintain and Version the huge image/Video data at on prem tool/clouds
Manage Models artifacts at model registry (in house or open tool)
Benchmark the Model across the models' versions and platforms
Skills
ML infrastructure
CPU/GPU
distributed storage
networking
CI/CD
model training
model evaluation
model packaging
edge/cloud deployment
monitoring
alerting
image/Video data versioning
model registry
AI/ML
Benefits
Hybrid work
Adaptable hours
Summer flex fridays
Focus fridays
Wellbeing day
Overview:
At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges.
Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.
You’ll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about—locally and globally.
Come make an impact every day at Zebra.
What We’re Looking For:
Analyzes, designs, develops, and maintains software solutions for the organization’s products and systems. Leads system integration of software and hardware to ensure consistent performance and program reliability. Develops, validates, and tests software functionality and user documentation. Work is evaluated upon completion to ensure objectives and quality standards are met. Determines and develops innovative approaches to solve complex problems, leveraging technical expertise and industry best practices.
Essential Duties and Responsibilities:
•
Design, build, and scale core ML infrastructure across heterogeneous compute (CPU/GPU), distributed storage, and networking resources.
•
Develop and maintain end-to-end CI/CD automation pipelines for continuous machine learning model training, evaluation, packaging, and edge/cloud deployment.
•
Implement comprehensive monitoring and alerting solutions to track real-time model performance, detect data/concept drift, and ensure sustained inference accuracy.
•
Automate the end-to-end model life cycle to streamline transitions from experimentation to high-availability production environments.
•
Drive operational maturity and system reliability to increase deployment velocity and support rapidly expanding AI/ML workloads.
•
Maintain and Version the huge image/Video data at on prem tool/clouds.
•
Manage Models artifacts at model registry(in house or open tool).
•
Benchmark the Model across the models’ versions and platforms.
Job Requirements:
• Bachelors Degree or Advanced Degree in Computer Science, Software Engineering, or a related engineering discipline
• 6+ years of relevant software engineering experience. 3+ years of experience with an Advanced Degree
• Equivalency: Equivalent work experience will be considered in lieu of a degree
Key Skills and Competencies:
•
Design, build, and scale core ML infrastructure across heterogeneous compute (CPU/GPU), distributed storage, and networking resources.
•
Develop and maintain end-to-end CI/CD automation pipelines for continuous machine learning model training, evaluation, packaging, and edge/cloud deployment.
•
Implement comprehensive monitoring and alerting solutions to track real-time model performance, detect data/concept drift, and ensure sustained inference accuracy.
•
Automate the end-to-end model life cycle to streamline transitions from experimentation to high-availability production environments.
•
Drive operational maturity and system reliability to increase deployment velocity and support rapidly expanding AI/ML workloads.
•
Maintain and Version the huge image/Video data at on prem tool/clouds.
•
Manage Models artifacts at model registry(in house or open tool).
•
Benchmark the Model across the models’ versions and platforms.
Benefits:
We understand the importance of work-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well-being day to promote revitalization and success.
Job Posting Statement:
To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. If you are a victim of identity theft contact your local police department.
AI Technology Statement:
Hiring process
Zebra Technologies leverages AI technology to evaluate job applications using objective, job-relevant criteria. This approach enhances efficiency and promotes fairness in the hiring process. However, every decision regarding interviews and hiring is made by our dedicated team, because we believe people make the best decisions about people. For more on how we use technology in hiring and how we process applicant data, see our Zebra Privacy Policy.