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About the role
Key Responsibilities Design and implement RTL architectures for real-time motion control, sensor fusion, and neural network inference. Translate complex control algorithms and machine learning models into efficient hardware pipelines . Optimize logic for latency, area, power, and reliability under real-time constraints. Collaborate with system architects, control engineers, and AI teams to define hardware specifications. Integrate custom RTL blocks into SoCs and FPGA-based platforms for flight control and autonomy. Drive RTL simulation, test bench development, synthesis, timing analysis , and hardware bring-up . Participate in design reviews and contribute to hardware/software co-design strategies. Requirements Qualifications 5\6 years of hands-on experience in RTL design using Verilog/SystemVerilog . Strong background in motion control systems , including servo control, path planning, and kinematics. Proficiency in implementing fixed-point and floating-point computation for control and AI workloads. Experience in neural network accelerators , MAC units, and quantized inference engines. Deep understanding of digital signal processing , real-time control, and hardware architecture. Familiarity with standard bus protocols (AXI, AHB) and memory subsystems (SRAM, DDR). Proficient in RTL simulation (ModelSim, VCS), synthesis, linting, and STA tools. B.E./B.Tech or M.E./M.Tech in Electrical\ / Electronics\ / Computer Engineering or equivalent. Preferred Qualifications Prior experience in aerospace or robotics hardware design. Exposure to FPGA-based control systems and flight computers. Understanding of UAV dynamics , sensor fusion (IMU, GPS, LiDAR), and control loops (PID, MPC). Familiarity with hardware/software co-simulation and embedded systems. Experience with HLS tools , and integration with C/C++ models or AI frameworks (TensorFlow Lite, ONNX).
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