Senior Machine Learning Engineer

Senior Machine Learning Engineer, Spatial Systems About GroundedAI 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, geospatial data, and spatial computing. Our focus is on shipping robust systems used by engineers in the field, not prototypes or research demos. Role Overview We're looking for a Senior Machine Learning Engineer to join our product engineering team and build the models that turn raw underground scan data into structured, trustworthy information. This role is ideal for someone with strong applied machine learning experience, particularly in 3D point cloud segmentation and detection, who also has genuine breadth to contribute to structured extraction from text. This is an applied machine learning engineering role first, with point cloud and spatial data as the core problem space. What You'll Do • Build models that assist in localization and automated quality checks • Develop models for geological prediction from limited expert input • Build segmentation and detection models for 2d and 3d model spaces • Contribute to structured extraction from technical documents, as your breadth allows • Work with noisy, inconsistent, real-world sensor data rather than clean benchmark datasets • Collaborate with the engineers working on geometry and localization • Contribute to technical discussions on model architecture, training data strategy, and what's realistic to ship near-term versus what needs a dedicated research track What We're Looking For Required • Strong applied machine learning experience, including real production work, not only research or coursework • Direct experience with 3D point cloud data: segmentation, detection, or reconstruction • Comfort working with noisy, real-world data where the "correct" answer isn't always well defined • Genuine breadth beyond point cloud work, ideally some experience with NLP or structured information extraction • Ability to work independently on problems without an established playbook • Strong software engineering fundamentals: production code quality, testing, and the ability to take a model from prototype to shipped feature Nice to Have • Experience with geospatial, geological, or industrial sensor data • Familiarity with point cloud libraries and frameworks (Open3D, PyTorch3D, or similar) • Experience building or fine-tuning LLM-based extraction pipelines • Background in mining, tunnelling, construction, or another physical-world industrial domain • Experience generating or working with synthetic training data for underrepresented data types Why GroundedAI • Own a genuinely novel technical problem with no existing playbook to follow • Work directly on the technology that turns field data into decisions with real financial stakes for clients • Small, senior team where your work ships to production, not a research pipeline • Competitive compensation and equity in the early stages of a funded startup • Flexible hybrid / remote work options • Support for learning, experimentation, and professional growth