Senior Solutions Architect - Data Labs

Invisible Technologies, London - Hybrid +5

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.

What you will 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, 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.

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.

Bonus and equity is included in all full-time, non commission offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process.

You can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.

What It's Like to Work at Invisible

At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI.

We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in.

Country Hiring Guidelines

Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process.

AI Interviewing Guidelines

Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey. Click here to learn more about how we use AI and our guidelines for candidates.

Senior Solutions Architect - Data Labs

The role

Senior Solutions Architect - Data Labs

The role

What you will do

What you need

Nice to have

What It's Like to Work at Invisible

Country Hiring Guidelines

AI Interviewing Guidelines

Accessibility Statement

About Invisible Technologies

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