ECE/Electronics Design Engineer

Role Overview: You will be part of a team that is developing AI systems for semiconductor and engineering workflows. Your expertise in EDA, ASIC, FPGA, VLSI, Verification, Physical Design, and Analog/Mixed Signal engineering will be crucial in training and evaluating next-generation AI models. This position offers a unique opportunity to merge deep domain knowledge with cutting-edge AI development. Key Responsibilities: - Create and review AI training data based on actual semiconductor workflows. - Evaluate AI-generated engineering solutions and pinpoint failure cases. - Break down intricate design, verification, and implementation tasks into structured workflows. - Assist in constructing benchmarks and datasets for AI systems utilized in semiconductor engineering. Qualifications Required: Must Have: - Hold a B.Tech / M.Tech / PhD in ECE, EE, VLSI, Microelectronics, or related disciplines. - Possess at least 2 years of industry experience in semiconductor, ASIC, FPGA, VLSI, Verification, Physical Design, Analog/Mixed Signal, or EDA sectors. - Hands-on familiarity with tools like Cadence, Synopsys, Siemens EDA, Ansys, or equivalent. - Exhibit strong problem-solving skills along with clear technical communication and documentation abilities. Good to Have: - Proficiency in Python or scripting/automation. - Experience in RTL design, STA, DFT, UVM, or analog simulation flows. - Background in AI/ML-assisted EDA or exposure to LLM-based tools. Additional Details: Ideal candidates for this role include engineers from prominent semiconductor companies, research labs, or rapidly growing startups. Graduates from esteemed institutions such as IITs, IISc, BITS, top NITs, or leading international universities are encouraged to apply. We are seeking individuals who are self-driven, possess profound technical expertise, and showcase strong reasoning and documentation skills. If you believe you are the right fit for this role, please submit your CV along with a brief description of your domain experience and the tools you have worked with. Role Overview: You will be part of a team that is developing AI systems for semiconductor and engineering workflows. Your expertise in EDA, ASIC, FPGA, VLSI, Verification, Physical Design, and Analog/Mixed Signal engineering will be crucial in training and evaluating next-generation AI models. This position offers a unique opportunity to merge deep domain knowledge with cutting-edge AI development. Key Responsibilities: - Create and review AI training data based on actual semiconductor workflows. - Evaluate AI-generated engineering solutions and pinpoint failure cases. - Break down intricate design, verification, and implementation tasks into structured workflows. - Assist in constructing benchmarks and datasets for AI systems utilized in semiconductor engineering. Qualifications Required: Must Have: - Hold a B.Tech / M.Tech / PhD in ECE, EE, VLSI, Microelectronics, or related disciplines. - Possess at least 2 years of industry experience in semiconductor, ASIC, FPGA, VLSI, Verification, Physical Design, Analog/Mixed Signal, or EDA sectors. - Hands-on familiarity with tools like Cadence, Synopsys, Siemens EDA, Ansys, or equivalent. - Exhibit strong problem-solving skills along with clear technical communication and documentation abilities. Good to Have: - Proficiency in Python or scripting/automation. - Experience in RTL design, STA, DFT, UVM, or analog simulation flows. - Background in AI/ML-assisted EDA or exposure to LLM-based tools. Additional Details: Ideal candidates for this role include engineers from prominent semiconductor companies, research labs, or rapidly growing startups. Graduates from esteemed institutions such as IITs, IISc, BITS, top NITs, or leading international universities are encouraged to apply. We are seeking individuals who are self-driven, possess profound technical expertise, and showcase strong reasoning and documentation skills. If you believe you are the right fit for this role, please submit your CV along with a brief description of your domain experience and the tools you have worked with.