About the job
Join Path Robotics
At Path Robotics, we are transforming the landscape of autonomous welding. Our innovative AI-driven robotic welding systems leverage advanced sensing and computer vision technologies to adapt in real time — eliminating the need for fixtures, programming, and operational limits. Join us in shaping the future of manufacturing as we seek passionate engineers eager to dive into hands-on work.
Your Role
We are in search of a Software Engineer to develop and sustain the infrastructure that underpins our AI and machine learning workflows. This position acts as a vital link between our AI research team and production — crafting the tools, pipelines, and deployment systems necessary to transition models from experimental stages to the factory floor. Ideal candidates are system thinkers fluent in ML, capable of understanding researchers' needs and constructing reliable software solutions around them.
Your Responsibilities
- Design and maintain the software infrastructure that supports robotic learning, encompassing model training, experiment tracking, version control, and deployment.
- Create pipelines and tools for the collection, processing, and curation of extensive robotic sensor and telemetry data.
- Facilitate the deployment of AI models onto robotic systems, including simulation, hardware integration, runtime monitoring, and feedback loops derived from field data.
- Develop internal tools that expedite AI engineering workflows related to dataset exploration, testing, evaluation, and productionization.
- Collaborate closely with AI researchers and robotics engineers to translate model requirements into robust, scalable software systems.
Your Profile
- 3–6 years of experience in software engineering, with strong exposure to ML infrastructure, MLOps, or AI systems.
- Proficient in writing production-level software in Python, with additional familiarity in C++, ROS, or robotics software stacks preferred.
- Experience with PyTorch and a solid grasp of the software systems necessary for AI model development.
- Comfortable working with data pipelines, simulation environments, containerized workflows, and GPU computing within Linux environments.
- Enthusiastic about collaborating across AI and robotics domains.

