Full-time or part-time: Full-time

Job title: Lead Engineer, Edge Intelligence

Job Location: 2700 Sandhill Road, Menlo Park, CA 94025

Job Description:

Responsible for the technical efforts related to intelligence (ex: Machine Learning (ML) / Artificial Intelligence (AI) based workloads, advanced algorithms, data processing) closer to our operations (Edge). Work closely with other technical leaders from across the company to iterate on identified opportunities, and to own critical pieces to drive their technical delivery. Architect, design and implement compelling technical solutions in the areas of ML/AI, as applied to the Edge. Develop concrete Architectural blueprints for components under ownership. Iterate with other team members, Engineers to translate Architecture into design and implementation. Articulate the pros and cons of various available choices. Collaborate directly with stakeholders to understand the challenges and opportunities, explore the solution space, design and lead the development of viable solutions to various challenges. Follow the technology evolution in this space to maintain expertise on the latest advances in the edge computing domain. Identify and work closely with vendors and partners from the available technology ecosystem to accelerate solution creation.

Compensation for this role between $156,520 - $166,520/year

Minimum Education & Experience Requirements:

Must have a Bachelor's Degree, or foreign educational equivalent, in Computer Science, Electrical Engineering, or a directly related IT degree. Must have 3 years of post-baccalaureate experience as a Software Engineer. The 3 years of experience must include experience with Algorithms and Data Structures fundamentals to allow for stable, scalable software solutions; applied Machine Learning and Artificial Intelligence frameworks (including Computer Vision, time series analysis, data science); diverse edge architectures, including implications of employing various compute architectures such as Central Processing Unit, Graphical Processing Units, Tensor Processing Units and other specialized hardware to efficiently execute the ML/AI workloads at the edge; resource constraints at edge deployments and strategies to optimize their use; software solutions related to IoT (including services available through public cloud vendors, open source eco system and industry standards); developing solutions from various public cloud providers (such as Google Cloud Platform, Microsoft Azure) to build and operate end-to-end solutions; distributed computing concepts and frameworks (including Docker compose, Kubernetes, microservices, etc.).