Staff Engineer, Automotive Software Algorithm Engineering

Analog Devices — India, Bangalore, RMZ

& Scope • Define, Develop, prototype, and productize automotive in‑car infotainment audio algorithms across the cabin audio signal chain, including (but not limited to) algorithms like Acoustic Echo Cancellation (AEC), Acoustic & voice cancellation, Road Noise Cancellation (RNC), Personalized Sound Zones (PSZ), In‑Car Communication (ICC), Voice activity detection, trigger word recognition, etc. • Own algorithm lifecycle activities: requirements interpretation, algorithm design, simulation, development, validation, and tuning, translating concepts into robust, production‑ready implementations • Execute embedded implementation and optimization of algorithms on target ADI DSP platforms and relevant SoC platforms (ARM‑based and similar), ensuring real‑time performance and memory efficiency • Integrate audio algorithms into automotive embedded software stacks and frameworks, ensuring functionality, performance, and reliability on target platforms and reference systems • Contribute to system‑level integration and verification of real‑time automotive products, including debugging complex multi‑domain issues spanning algorithm, firmware, and platform layers • Collaborate with cross‑functional stakeholders (systems, firmware/platform, QA, product, customer teams) to refine feature requirements, validate behavior in real vehicles, and support customer integration needs • Apply AI/ML methods where beneficial (e.g., model‑assisted estimation, adaptive scene classification, learning‑based enhancement) to improve robustness and user experience (role requirement). • Use agentic and AI‑enabled productivity tools across the engineering lifecycle (e.g., AI‑assisted coding, test generation, documentation acceleration, and automated analysis) to improve quality, throughput, and traceability (role requirement). • Document engineering work products (design notes, configuration/guidelines, verification evidence) in alignment with structured development expectations (like ASPICE) typically

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