Sr. AI Researcher, Agentic Systems

Join an innovative team developing advanced AI systems that transform how complex construction projects are understood and managed. As a Senior AI Researcher specializing in agentic systems, you will build intelligent agents capable of reasoning across multimodal construction data, including 360° imagery, point clouds, BIM models, drawings, and technical specifications. Your work will bridge applied research and production engineering, turning cutting-edge AI capabilities into reliable, actionable insights. You will design agent architectures, retrieval systems, evaluation frameworks, and tools that help models answer real-world questions with verifiable evidence. Working closely with computer vision and platform engineering teams, you will shape the next generation of spatial AI applications. This remote opportunity is ideal for an experienced ML professional who thrives on complex challenges, rapid experimentation, and measurable real-world impact.

The ideal candidate combines advanced machine learning expertise, strong software engineering capabilities, and sound research judgment. You should be comfortable building production-grade AI systems, evaluating their limitations rigorously, and working with multimodal and spatial data.

• Master's or Ph.D. in a relevant field, or equivalent professional experience.

• At least 5 years of experience building machine learning systems, including experience taking at least one LLM- or VLM-based system into production and maintaining it over time.

• Demonstrated ability to design evaluation frameworks from scratch when reliable ground truth is sparse, costly, or contested.

• Strong knowledge of multimodal AI, including vision-language models, embeddings, retrieval systems, context engineering, and fine-tuning when justified by performance or cost.

• Strong software engineering skills, particularly in Python, asynchronous orchestration, tracing, and cost instrumentation.

• Ability to build custom testing and evaluation harnesses for AI systems.

• Familiarity with spatial data concepts, including coordinate frames, camera poses, and projections.

• Strong technical judgment in determining which tasks are appropriate for automation and when a system is not yet reliable enough to provide an answer.

• Ability to collaborate effectively with research and engineering teams, communicate findings clearly, and translate experimental results into production-ready solutions.

• Experience with post-training techniques or reinforcement learning applied to agent trajectories.

• Familiarity with document understanding for technical drawings, specifications, and other structured PDF documents.

• Experience working with additional sensor data, including mobile capture, wearable devices, or field audio.

• Background in construction, robotics, geospatial technology, or another domain where AI models interact with the physical world.