Job Description

About Us
We are an advanced technology company pioneering the fusion of classical hardware design with modern AI. Our systems push the boundaries of performance and automation in edge computing and chip design, We believe the future of hardware is AI-assisted, and we're building that future now.

We re looking for a RTL/Firmware Design Engineer who thrives at the intersection of traditional hardware development and cutting-edge AI. This is a cross-disciplinary role for a designer who can move comfortably between RTL (Verilog/SystemVerilog/VHDL), firmware (C/C++/Rust), and Python-based AI workflows and who is eager to apply Deep Reinforcement Learning and Graph Neural Networks to accelerate RTL design, verification, and optimization tasks.
Responsibilities

• Design and verify via testbenches or FPGA prototypes , , RTL for custom logic in FPGAs or ASICs.
• Develop embedded firmware in C/C++ or Rust to support hardware functionality and validation.
• Use tools like Cursor and Windsurf to explore RTL design spaces and assist with floorplanning, synthesis, and physical design optimization.
• Leverage Deep Reinforcement Learning to automate and enhance RTL-related tasks
• Design and apply Graph Neural Networks (GNNs) for learning representations of circuits, netlists, or control/data flow graphs.
• Build AI agents using PyTorch, Hugging Face, and RL libraries such as RLlib, Stable-Baselines3, or CleanRL.
• Collaborate with team to develop novel AI-augmented hardware design workflows.
• Contribute to internal tools and libraries that merge ML/AI with RTL and firmware workflows.

• 3 6 years of hands-on experience in RTL design and firmware development.
• Proficiency with Verilog, SystemVerilog.
• Experience with embedded software development in C/C++ or Rust.
• Familiarity with FPGA or ASIC development flows, including synthesis, place and route, and timing closure.
• Practical experience using Cursor, Windsurf, or similar hardware exploration tools.
• Python programming skills and deep knowledge of PyTorch.
• Solid understanding of Deep Reinforcement Learning and hands-on experience with RL frameworks.
• Exposure to Graph Neural Networks (GNNs) and their application in structured data or graph-based modeling.
• Experience with /exposure to Agentic workflows using Crew.ai, Langflow, n8n, Autogen or other similar agentic frameworks
• Experience with RTL simulation frameworks like Verilator or cocotb.

• Experience applying AI/ML to EDA problems such as floorplanning, verification, testbench generation, or synthesis optimization.
• Familiarity with ONNX, TorchScript, or exporting ML models for runtime integration.
• Contributions to open-source ML/EDA/hardware projects or relevant research publications.