Senior Solutions Architect - Data Labs at Invisible Technologies in New York, NY

Job description

About Invisible Invisible is an Applied AI research company closing the gap between frontier models and the enterprise. We provide the platform infrastructure that makes AI work in production at scale: unify fragmented data, map complex processes, orchestrate and evaluate agents, and stand up hyper-personalized workflow applications fast for companies like Grubhub, Alliance Global, and Thomson Reuters. Where models fall short, we build RL environments and data to fix them. The result: a virtuous cycle between enterprise deployment and model training. Recognized as Best Predictive Analytics Platform of the Year at the 2026 AI Breakthrough Awards, Invisible makes AI work. About the Role Invisible's Data Lab works with leading AI companies to design and deliver the human data that advances model capabilities and measures performance. Our work spans the full frontier of AI training and evaluation—from supervised fine-tuning and preference data to benchmarks, red teaming, multimodal data, coding tasks, and agent environments. Solutions architects are at the center of this work, working with clients to define the problem and translating open-ended model development problems into scalable programs with sound methodology. We are looking for a Senior Solutions Architect to bring technical depth, sharp commercial judgment, and a strong command of the rapidly evolving AI training and evaluation landscape. Embedded within an account team working with a major frontier AI lab, you will lead solution design for new opportunities and pilots. You will partner with Project Leads who own execution, client management, and overall project outcomes, while remaining accountable for the integrity, feasibility, and scalability of the solution through handoff and early delivery. You will often enter when a client's ask is still underspecified. Your job is to understand what they are really trying to achieve, translate that into a collection we can execute, and help the client and our teams make the right technical, methodological, and commercial choices. You will connect the perspectives of client researchers, program managers, Project Leads, experts, and engineers—bringing context that may not otherwise exist in the room. This is not a traditional infrastructure architecture role, nor is it primarily a project management role. It sits at the intersection of ML research, human data, product thinking, and client partnership. Success means teams make better decisions because you are involved: projects are framed correctly, solutions reflect the state of the art, and clients trust Invisible to understand where their work is going next. You will work across a remarkable range of frontier AI problems, with direct exposure to the researchers and program leaders defining them. You will help determine how novel human-data programs should work before there is an established playbook, influence the products and systems used to deliver them, and build reusable ways of thinking that shape how Invisible approaches the field. If you live and breathe LLM training and evaluation, communicate with unusual clarity, and want your judgment to matter in the room, this role offers a rare combination of technical range, client proximity, and real-world impact. What You'll Do Equip account teams with the technical and research context they need to fully understand client requests and make sound decisions about how to approach them. Own the solution design of new human-data projects and pilots, working with Project Leads from early discovery and scoping through the transition into execution. Work directly with client research and program management stakeholders to interpret ambiguous requests, uncover the underlying objective, and turn it into a clear and executable approach. For technical POCs in particular, you may take the lead in the relationship alongside the Project Lead. Advise on methodology/best practices across supervised fine-tuning, preference data and RLHF, benchm