Robotics engineer

Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall. Responsibilities Architect and lead the design of complex simulation environments in Isaac Sim, and other industry-standard platforms. Own the simulation testing strategy — design comprehensive experiment frameworks to validate robot behavior, stress-test edge cases, and ensure production readiness. Drive automation initiatives — build and maintain robust testing pipelines, integrate simulation seamlessly into CI/CD workflows, and establish best practices for the team. Establish documentation standards and create technical specifications that enable knowledge sharing across engineering teams. Qualifications 5+ years of professional experience in robotics simulation or related fields. Expert-level Python programming with proven ability to write maintainable, performant code for complex systems. Deep expertise with ROS/ROS 2 architecture, including custom node development, message design, and system integration. Production experience with multiple simulation platforms (Isaac Sim, Gazebo, or equivalent) in real-world robotics projects. Strong theoretical foundation in robotics fundamentals — kinematics, dynamics, control theory, and trajectory planning. Proven track record of technical leadership — ability to make architectural decisions, drive technical discussions, and influence engineering direction. Experience working in fast-paced startup or product-driven environments with ability to balance technical excellence with business priorities. Bonus Skills: Professional experience with industrial robotics (KUKA, FANUC, ABB, Universal Robots) and robot programming languages (KRL, RAPID, LS). Advanced 3 D modeling and CAD skills (Blender, Solid Works, Fusion 360) including URDF/SDF creation and optimization. Hands-on experience with GPU-accelerated simulation and physics engine optimization (Phys X, Bullet, ODE). Machine learning/AI integration in simulation environments — reinforcement learning, sim-to-real