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Principal Engineer - Automotive(FuSa/embedded Systems)

5 - 10 years

5 - 10 Lacs

Bengaluru / Bangalore Karnataka India

Posted:3 weeks ago| Platform: Foundit logo

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Code Coverage Tool Principal Engineer Automotive fusa embedded systems

Work Mode

On-site

Job Type

Full Time

Job Description

Key Responsibilities Hands-on contribution is expected on evolving family of Automotive chipsets for lead Auto customers Responsibilities areas include Automotive Functional Safety, AI Model Onboarding & Performance tuning, Chipset bring-up (AI/ML Sub-systems) for newer Automotive platforms Drive deliverables against requirements coming from Automotive customers and internal teams. Tight collaboration with various stakeholders across the globe to deliver on various commitments (internal and customer milestones) Capability to comprehend customer requirements and disseminating those within the development and test teams Lead an established team and look for opportunities to grow the team in the areas of AI/ML applicable to automotive segment Ability to adapt to changing priorities and nature of activities needed to address the instantaneous needs of the team Hiring and mentoring the team to meet their professional aspirations Qualifications: Bachelor's / Masters/ PhD degree in Computer Science or related fields 18+ years Software Engineering or related work experience. 15+ years experience with Programming Language such as C, C++, Python. Desirable Skills and Aptitudes Automotive experience, Functional Safety, and related practices Embedded software, driver development on SoCs using Linux/Android/QNX etc Experience of profiling software and optimization techniques Experience with Code Coverage, Code Quality Tools used in Automotives. Exposure to Parasoft for MISRA compliance would be a plus Excellent C/C++/Python programming and software design skills, including debugging, performance analysis, and test design Experience with software development processes such as Automotive ASIL-A/B, a plus Knowledge of the different accuracy techniques and exposure on improving neural network accuracy across different precisions is a plus

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