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Invisible Technologies is an AI platform company that structures messy data, automates digital workflows, and deploys agentic solutions. The company has reached $134M in revenue, is profitable, and recently raised $100M in growth capital. Invisible works with leading AI companies including Microsoft, AWS, and Cohere to design and deliver human data that advances model capabilities.
The Senior Solutions Architect role sits within Invisible's Data Labs, which works with frontier AI companies on human data programs spanning the full spectrum of AI training and evaluation—from supervised fine-tuning and preference data to benchmarks, red teaming, multimodal data, coding tasks, and agent environments.
You will be embedded within an account team working with a major frontier AI lab. Your primary responsibilities include:
• Leading solution design for new opportunities and pilots, working with Project Leads from early discovery through execution handoff
• Translating underspecified client requests into clear, executable approaches by understanding underlying objectives
• Advising on methodology and best practices across supervised fine-tuning, preference data/RLHF, benchmarks, evaluations, red teaming, multimodal data, coding tasks, agent environments, and emerging approaches
• Shaping task design, annotation workflows, quality strategies, data structures, and trade-offs between research value, feasibility, speed, scale, and expert experience
• Defining expert profiles required for projects, including necessary knowledge, experience, and judgment
• Bringing commercial judgment to scoping, building estimates of effort, expert supply, timeline, and cost
• Partnering with Engineering to define expert-facing interfaces and technical architecture
• Prototyping or testing critical solution elements during discovery
• Building alignment across clients, account leadership, Project Leads, Engineering, and internal teams
• Staying deeply current on LLM training, post-training, human data, benchmarks, and evaluation research
• Developing and sharing reusable mental models and best practices across the organization
This is not a traditional infrastructure architecture or 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 their work.
You will work across a remarkable range of frontier AI problems with direct exposure to researchers and program leaders defining them. You will help determine how novel human-data programs should work before established playbooks exist, influence the products and systems used to deliver them, and build reusable thinking that shapes how Invisible approaches the field.
REQUIREMENTS:
Required:
• Deep, demonstrable interest in how frontier AI models are trained, improved, and evaluated; you actively follow the space and form your own views
• Strong working knowledge of LLM training and evaluation concepts, with ability to reason across data modalities, collection methods, and research objectives
• Exceptional communication skills: listen closely, identify gaps in underspecified requests, explain complex ideas clearly, influence technical and non-technical stakeholders
• Judgment to challenge assumptions constructively while maintaining momentum and client trust
• Technical fluency in ML, data systems, software architecture, APIs, and data flows to collaborate credibly with researchers and engineers
• Commercial judgment and comfort with rough-cut budgeting, weighing research ambition against expert availability, delivery effort, timeline, cost, and unit economics
• Ability to synthesize client context, research intent, expert capabilities, user experience, technical constraints, and operational realities into executable approaches
• Experience working across multiple stakeholders or teams in ambiguous environments where responsibilities and requirements continuously evolve
• High ownership, curiosity, adaptability, and low ego
Nice to Have:
• Background in computer science, software engineering, machine learning, data science, AI research, technical product, solutions architecture, or related field (equivalent knowledge through independent work equally welcome)
• Direct experience with human-data programs, data annotation, model post-training, RLHF, RLVR, evaluations, benchmarks, red teaming, or research operations
• Experience partnering directly with AI researchers, research engineers, or technical program managers
• Hands-on ability with Python, SQL, APIs, notebooks, or lightweight prototyping
• Experience designing tools or workflows for specialized users where expert judgment and data quality are central
• Evidence of serious engagement with the field through professional work, research, open-source contributions, technical writing, independent projects, benchmark participation, or self-directed learning
