Robotics Engineer

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter. Our base salary range is $118,800 - $178,200, and in addition we have generous bonus plans to provide a competitive compensation package.  Who You Are: You are a hands-on robotics engineer who thrives at the intersection of theory and system-building, equally comfortable developing low-level controllers as you are deploying learned policies on real hardware. You have experience working across the full robotics stack, from reasoning and perception to control and physical execution, and you take a systems-oriented view of complex robotic platforms. Whether working with real robots or high-fidelity simulators, you bring strong debugging instincts, fluency in Python, and a practical understanding of modern robotics and simulation ecosystems. You care about making systems actually work, not just in isolation, but end-to-end under real-world constraints. In this role, you will collaborate closely with researchers on high-impact robot learning projects, translating cutting-edge algorithms into robust, deployable behavior. You will help build and iterate on sim-to-real pipelines, develop control and experimentation infrastructure, and enable large-scale learning through GPU-accelerated simulation. This is an environment where engineering rigor meets research ambition: you’ll have the opportunity to shape core system capabilities, influence experimental design, and contribute to projects that push the boundaries of what robots can learn and do in the real world. Who We Are:  Ai2’s Robotics team is led by Dieter Fox. We aim to develop and leverage scalable simulation domains to scientifically investigate the scaling and generalization capabilities of foundation models for robotics, and use these insights to develop broadly competent robots. Following a longstanding tradition at Ai2, we are committed to fully open-source research and work toward application domains with a positive impact on society.  We are looking for applicants interested in robotics, with a focus on core techniques in robot manipulation, large scale training in simulated environments, sim-to-real transfer, robot foundation models, and skill learning.  We regularly publish in high-profile conferences and journals in robotics (CoRL, ICRA, RSS), computer vision (CVPR, ICCV, ECCV), and machine learning (e.g., NeurIPS, ICLR, ICML).  We collaborate frequently with researchers on other Ai2 teams focused on computer vision, large language models, and others. Our main office has close ties and unique access to researchers at the University of Washington, only 1.5 miles away. Researchers excited about such internal and external collaborations are encouraged to apply. Your Next Challenge: Our needs include the following competencies. If your experience and interests are a match for some but not all of these areas, we still encourage you to apply. Develop and maintain complete control stacks for robotic systems, including low level control, motion planning,  and integration of learned policies. Develop and maintain the sim-to-real pipeline, including system identification, understanding simulation models, and deploying learned policies from simulation to real hardware. Lead robot set-up across manipulation and mobile hardware platforms, including integration, calibration, and system tuning. Diagnose and resolve complex system behaviors across hardware, software, and runtime. Collaborate closely with researchers to translate algorithms into reliable, real-world robotic behavior. Work with modern GPU-accelerated simulation frameworks to enable large-scale experimentation. What You’ll Need: Minimum Qualifications:  Masters + 3 years experience or PhD in robotics or relevant field. Exceptional candidates with a  bachelor’s degree and 3+ years of industry experience are also welcome to apply. Proficiency in Python and modern robotics / simulation ecosystems. Strong depth of experience working with either physical robots or robotic simulation. Strong development and debugging skills across the full robotics stack (perception → planning or inference → control → hardware). Clear communication and ability to collaborate effectively with research teams. A structured, systems-oriented approach to building and improving complex robotic infrastructure.  Preferred Qualifications:  Experience implementing and tuning real-time control systems along with a strong foundation in control, dynamics, and system identification. Experience translating control and learning-based methods from simulation to real robotic systems. Familiarity with large-scale simulation and reinforcement learning pipelines. Physical Demands and Work Environment: The physical demands described here are representative