Software Engineer

GroundedAI builds software systems that turn complex, real-world data into reliable insights. Our products operate in challenging, industrial environments where correctness, performance, and maintainability matter just as much as innovation.

We work at the intersection of software engineering, applied machine learning, and geospatial data. Our focus is on shipping robust systems, not prototypes or research demos.

We're looking for a Software Engineer to join our core product engineering team. This role is ideal for someone with ~5 years of experience who enjoys building and owning production systems end-to-end, including services that incorporate machine learning and computer vision components.

You'll collaborate with engineers across the stack to design, build, deploy, and operate software that includes ML-powered features while maintaining high standards for reliability, performance, and code quality.

This is a software engineering role first, with machine learning as an important (but not exclusive) part of the problem space.

• Design, implement, and maintain production software systems that power GroundedAI's products
• Build backend services and pipelines that support data processing, model training, and inference
• Integrate computer vision and ML components into user-facing applications
• Improve system performance, reliability, and observability in production environments
• Collaborate with product, ML, and frontend engineers to deliver end-to-end features
• Debug and resolve complex production issues across services, data, and infrastructure
• Contribute to technical design discussions and help evolve engineering best practices

• ~5 years of professional experience as a software engineer (backend, platform, or product)
• Strong fundamentals in software design, debugging, testing, and code quality
• Experience building and operating production systems used by real users
• Proficiency in Python and experience with modern development workflows (Git, CI/CD)
• Comfort working with APIs, services, and data pipelines
• Experience deploying software using Docker and cloud infrastructure (AWS, GCP, or Azure)
• Ability to collaborate across disciplines and take ownership of technical problems

• Experience working with ML-backed or data-driven systems in production
• Familiarity with computer vision concepts such as image segmentation, feature extraction, or object detection
• Exposure to ML frameworks like PyTorch or TensorFlow
• Experience working with large datasets, geospatial data, or 3D / point-cloud data

• Work on real-world systems with meaningful constraints and impact
• Strong engineering culture focused on ownership, pragmatism, and quality
• Opportunity to influence core architecture and technical direction
• Competitive compensation and benefits
• Flexible hybrid / remote work options
• Support for learning, experimentation, and professional growth