SOFTWARE DEVELOPER | SOFTWARE ENGINEER | C++ DEVELOPER
Pubblicato il 13-08-2026 - Byte-Code in Italia
Xaba is at the forefront of AI-driven robotics automation, pioneering cutting-edge solutions to enhance industrial processes. Headquartered globally, with a team of collaborative scientists, engineers, and designers, Xaba operates in a remote-first and distributed environment. Join us to revolutionize the future of AI applied to robotics.
We’re looking for a C++ Software Engineer specialized in Robotics to join the team. You will be responsible for finding computational solutions to complex technical challenges, spearheading investigation in scalable distributed algorithms and development of our cloud-based systems, whilst providing technical leadership to junior team members.
Research, design and implement robust, scalable, high-performance computer algorithms
Investigate state-of-the-art computational methods by reading relevant academic papers and attending appropriate workshops/conferences
Collaborate with a multidisciplinary team including designers and software engineers in multiple locations
Mentor junior staff and interns to reach their best potential
D. in Computer Science,
Computer Engineering, Mathematics, Physics or related disciplines - or equivalent experience
Fluent in C++ and Python
Linear Programming, Mixed Integer Programming, Combinatorial Optimization, Constraint Programming, Dynamic Programming
Expertise in API design, component architectures, principles of service-oriented software
Experience using automated testing and continuous integration frameworks
Exposure to software product development and delivery practices and related tools
Familiarity with fundamental Modeling & Simulation principles and methods
These are the things you’re likely to work on, so if you have any experience in the following, great If you don’t have experience in the following, we still encourage you to apply.
Knowledge of core robotic aspects: inverse and forward kinematics, singularities, trajectory planning
Additional mathematical optimization methods: gradient estima