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The SDK Application software team is responsible for creating SDK and application framework for SiMa.ai’s MLSoC product line. This includes developing Python and C++ Framework for using our SDK, developing system level building blocks for AI applications, and developing sample applications for customers to replicate. All these applications are developed on Embedded Linux and the APIs are for enabling end2end customer application flow utilizing different IO subsystems that are part of MLSoC.
Responsibilities:
We are looking for a seasoned Engineering Manager who has significant experience in developing Linux applications in an embedded environment for infrastructure platforms, has a proven track record of working in a high-performance team and has partnered with cross functional teams across multiple geographies. Key responsibilities include (but not limited to):
Key Requirements:
Personal attributes:
Can-do attitude. Strong team player. Curious, creative, and good at solving problems. Execution and results oriented. Self-driven, Thinks Big and is highly accountable. Good communication skills.
The annual salary for this position ranges from $220,000 - $275,000. The actual annual salary paid for this position will be based on several factors, including but not limited to, skills, prior experiences, qualifications, expertise, work location, total target compensation, training, company needs, and current market demands. The annual salary range for this position is subject to change and may be adjusted in the future.
EEO Employer: SiMa is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
SiMa.ai stands as a pioneering machine learning company, offering the first-of-its-kind software-centric MLSoC platform designed with a specific purpose. Our platform ensures seamless deployment and scaling of machine learning at the embedded edge, providing customers with the ability to tackle any computer vision challenge with the added advantage of achieving 10 times better performance at the lowest power consumption. Initially concentrating o