Path Robotics
Software Engineer – ML Infrastructure
Columbus, OhiomidAdded today
About this role
Path Robotics seeks a Software Engineer to design and maintain ML infrastructure that bridges AI research and production robotics. You'll build deployment pipelines, data processing systems, and internal tools that enable models to transition from experiment to factory floor.
What you'll do
- Build and maintain software infrastructure for robot learning, including model training, experiment tracking, versioning, and deployment
- Develop data pipelines for collecting, processing, and curating large-scale robotic sensor and telemetry data
- Support AI model deployment onto robotic systems with simulation, hardware integration, monitoring, and field data feedback loops
- Create internal tools that accelerate AI engineering workflows across dataset exploration, testing, and evaluation
- Partner with AI researchers and robotics engineers to translate model requirements into scalable software systems
What they're looking for
- Python production software development
- ML infrastructure and MLOps
- PyTorch and deep learning frameworks
- C++ and/or ROS robotics software stacks
- Data pipelines and ETL systems
- Containerization (Docker, Kubernetes) and Linux environments
- GPU compute and distributed systems
- Simulation environments and hardware integration
Benefits
- Daily free lunch
- Flexible PTO
- Comprehensive medical, dental, and vision coverage
- 6 weeks paid parental leave, plus 6–8 weeks for birthing parents
- 401(k) retirement plan through Empower
- Generous employee referral bonuses
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Path Robotics
Path Robotics develops autonomous robotic welding systems with adaptive motion planning and AI-driven capabilities for manufacturing. The company is hiring welding engineers, mechanical engineers, machine learning engineers, and technical marketing engineers to advance its mobile robotic welding solutions.
- Website
- pathrobotics.com
Likely interview questions
- Describe a time you built or optimized an ML pipeline in production—what were the key challenges and how did you address them?
- How have you approached bridging the gap between research and production in ML systems, and what tools or practices did you use?