Senior Solutions Architect - Data Labs

From the listing

About the role

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, you may take the lead in the relationship alongside the Project Lead.
• Advise on methodology and best practices across supervised fine-tuning, preference data and RLHF, benchmarks and evaluations, red teaming, multimodal data, coding tasks, agent environments, and emerging approaches.
• Shape task design, annotation and evaluation workflows, quality and validation strategies, data structures, and the trade-offs between research value, feasibility, speed, scale, and expert experience.
• Define the expert profiles a project requires, including the knowledge, experience, and judgment needed to produce credible data.
• Bring commercial judgment to early scoping, building rough-cut estimates of effort, expert supply, timeline, and cost so teams can test feasibility and shape viable proposals.
• Partner with Engineering to define the expert-facing interfaces and technical architecture that collections require, including workflows, APIs, data exchange patterns, synthetic testing, and validation mechanisms.
• Prototype or test critical elements of a solution during discovery, while working with Engineering as the team responsible for production implementation.
• Build alignment across clients, account leadership, Project Leads, Engineering, expert operations, and other internal teams.
• Stay deeply current on LLM training, post-training, human data, benchmarks, and evaluation research, and translate new developments into practical implications for active and future client work.
• Develop and share reusable mental models, patterns, and emerging best practices that raise the technical fluency and solutioning quality of the wider organization.

What they're looking for

• Deep, demonstrable interest in how frontier AI models are trained, improved, and evaluated. You actively follow the space and can form your own views rather than simply repeat its vocabulary.
• Strong working knowledge of LLM training and evaluation concepts, with the ability to reason across data modalities, collection methods, and research objectives.
• Exceptional communication skills. You can listen closely, identify what is missing from an underspecified request, explain complex ideas clearly, and influence technical and non-technical stakeholders.
• The judgment to challenge assumptions constructively while maintaining momentum and client trust. You know when to ask another question, when to recommend a different approach, and when to make a pragmatic call.
• Enough technical fluency in ML, data systems, software architecture, APIs, and data flows to collaborate credibly with researchers and engineers and to shape implementable solutions.
• Commercial judgment and comfort with rough-cut budgeting, particularly when 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 an executable approach.
• Experience working across multiple stakeholders or teams, particularly in ambiguous environments where responsibilities and requirements continuously evolve.
• High ownership, curiosity, adaptability, and low ego. You care more about reaching the right answer and improving the outcome than being the person who supplied it.

Nice to have

• A background in computer science, software engineering, machine learning, data science, AI research, technical product, solutions architecture, or a related field. Equivalent knowledge gained through independent work or other professional experience is 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 tools such as Python, SQL, APIs, notebooks, or lightweight prototyping. You will not be the primary builder, but being able to inspect, test, or demonstrate an idea can make you more effective.
• Experience designing tools or workflows for specialized users, particularly where expert judgment and data quality are central to the outcome.
• Evidence of serious engagement with the field through professional work, research, open-source contributions, technical writing, independent projects, benchmark participation, or other forms of self-directed learning.

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