Sr. AI Researcher, Agentic Systems

Accountabilities:: As a senior contributor to applied AI research and development, you will design, evaluate, and deploy agentic systems that reason across complex multimodal datasets while ensuring their outputs are accurate, traceable, and useful in real-world workflows. Design and develop agentic systems capable of planning and executing multi-step tasks across imagery, spatial geometry, and technical documents. Build and maintain the context and retrieval layer, enabling agents to access relevant evidence from spatially and temporally indexed construction records. Develop the tools and interfaces agents use to locate components, compare site captures, inspect drawing regions, and query BIM elements. Design evaluation frameworks for complex tasks where ground truth is limited, expensive to obtain, or subject to interpretation. Use evaluation results to determine system readiness, identify limitations, and guide improvements before production deployment. Select the most appropriate model for each task, balancing frontier-model APIs, open-weight models, fine-tuned smaller models, and classical computer vision techniques against cost, latency, and accuracy requirements. Ensure generated outputs are traceable by connecting claims to their supporting evidence in images, drawing sheets, or specification sections. Prototype new approaches rapidly, discontinue ineffective solutions, and collaborate with platform engineers to bring successful systems into production. Monitor and optimize deployed systems to maintain reliability, performance, and cost efficiency at scale. Requirements: 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. Preferred qualifications: 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. Benefits: Base salary: $184,000–$265,000 annually, depending on location, skills, experience, training, qualifications, and other business considerations. Equity awards as part of the total rewards package. Medical, dental, and vision coverage with 100% of premiums paid by the employer. Flexible paid time off. 401(k) retirement plan with company matching. Paid parental leave. Home office stipend. Employee referral program. Annual company retreats and team gatherings. Region-specific health and wellness benefits. Remote work arrangement within the United States.