Senior Edge Intelligence Engineer
2 weeks ago
Job Title: Lead Engineer, Edge Intelligence
Location: Not specified
Role Overview:
The Lead Engineer for Edge Intelligence is accountable for spearheading technical initiatives related to intelligence, particularly in the realms of Machine Learning (ML) and Artificial Intelligence (AI) workloads, advanced algorithms, and data processing at the edge of operations.
This role involves close collaboration with other technical leaders across the organization to identify opportunities and take ownership of critical components to ensure their successful technical delivery.
Key responsibilities include architecting, designing, and implementing innovative technical solutions in ML/AI as applied to edge computing. The engineer will develop comprehensive architectural blueprints for components under their purview and work alongside team members and engineers to translate architecture into practical design and implementation.
Additionally, the role requires articulating the advantages and disadvantages of various available options and collaborating directly with stakeholders to understand challenges and opportunities. The engineer will explore the solution landscape, design, and lead the development of feasible solutions to various challenges.
Staying abreast of technological advancements in the edge computing domain is essential to maintain expertise. The engineer will also identify and engage with vendors and partners within the technology ecosystem to expedite solution development.
Compensation: The salary range for this position is between $156,520 - $166,520 per year.
Education & Experience Requirements:
Applicants must possess a Bachelor's Degree or a foreign equivalent in Computer Science, Electrical Engineering, or a closely related IT discipline.
- Proficiency in Algorithms and Data Structures fundamentals to create stable and scalable software solutions.
- Experience with applied Machine Learning and Artificial Intelligence frameworks, including Computer Vision and data science.
- Familiarity with diverse edge architectures and the implications of utilizing various compute architectures such as CPUs, GPUs, and TPUs for efficient execution of ML/AI workloads.
- Understanding of resource constraints in edge deployments and strategies for optimization.
- Experience with IoT-related software solutions, including services from public cloud vendors and industry standards.
- Development of solutions using public cloud providers to build and operate comprehensive end-to-end solutions.
- Knowledge of distributed computing concepts and frameworks, including Docker, Kubernetes, and microservices.
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