Senior Solutions Architect - Data Labs

Invisible Technologies
London, New York, San Francisco
Workplace: HybridFull timeFunction: Solutions Engineering & Sales EngineeringSkills: ["Communication","Stakeholder influence","Judgment","Ownership","Adaptability"]

Design and lead solution architecture for human-data projects and pilots, translating underspecified client asks into scalable, executable programs. Partner with Project Leads, client researchers, and program management to interpret objectives, choose methodology across LLM training/evaluation, and ensure integrity, feasibility, and delivery readiness from discovery through early implementation. Collaborate with Engineering on interfaces and data exchange workflows, prototype critical elements, and continuously update best practices and reusable patterns for the organization.

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Invisible Technologies
Invisible Technologies
22 hours ago

Senior Solutions Architect - Data Labs

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Last checked: 12 hours agoStatus: Live

Job Summary

Design and lead solution architecture for human-data projects and pilots, translating underspecified client asks into scalable, executable programs. Partner with Project Leads, client researchers, and program management to interpret objectives, choose methodology across LLM training/evaluation, and ensure integrity, feasibility, and delivery readiness from discovery through early implementation. Collaborate with Engineering on interfaces and data exchange workflows, prototype critical elements, and continuously update best practices and reusable patterns for the organization.
Location: London, New York, San Francisco
Workplace: Hybrid
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering
Seniority: Mid level

Key Responsibilities

  • •Lead solution design for new human-data projects and pilots with Project Leads from early discovery and scoping through transition into execution.
  • •Interpret ambiguous client requests, uncover underlying objectives, and translate them into clear, executable approaches for technical POCs when needed.
  • •Advise on methodology and best practices across supervised fine-tuning, preference data and RLHF, benchmarks and evaluations, red teaming, multimodal data, coding tasks, and agent environments.
  • •Shape task design, annotation and evaluation workflows, quality/validation strategies, and trade-offs between research value, feasibility, speed, scale, and expert experience.
  • •Partner with Engineering to define expert-facing interfaces and technical architecture (workflows, APIs, data exchange patterns, synthetic testing, and validation mechanisms) and prototype critical solution elements during discovery.

Pay and Benefits

Equity and Bonus:Equity
Perks:EquityAnnual Bonus

Key Requirements

  • •Strong, demonstrable interest in how frontier AI models are trained, improved, and evaluated, with the ability to form independent views.
  • •Deep working knowledge of LLM training and evaluation concepts across data modalities, collection methods, and research objectives.
  • •Exceptional communication to listen closely, clarify complex ideas, identify missing information, and influence technical and non-technical stakeholders.
  • •Technical fluency across ML, data systems, software architecture, APIs, and data flows to collaborate credibly with researchers and engineers.
  • •Commercial judgment for rough-cut budgeting and decision-making when balancing research ambition against feasibility, delivery effort, timeline, cost, and unit economics.
Experience:Frontier AILLM trainingModel evaluationHuman dataAI research
Skills:CommunicationStakeholder influenceJudgmentOwnershipAdaptability
Tech Stack:PythonSQLAPIsNotebooks

Company Brief

Invisible Technologies
Invisible Technologies provides an enterprise AI platform that structures messy data, builds digital workflows, deploys agentic solutions, and integrates human expertise to operationalize AI for customers across industries. Founded in 2015, it has rapidly grown and serves large enterprise clients.
Industry: AI & Machine Learning
Company Size: Large (251 to 1,000 employees)
Revenue: USD 100M to 250M
Growth: Scaleup
Funding: Series A
Headquarters: New York, United States
Founded: 2015
WebsiteLinkedIn