Head of Frontier AI

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 RL Data Lab

Invisible's RL Data Lab builds the data and evaluation infrastructure that model builders use to train and measure their models, from foundation labs to large tech companies to enterprises building their own agentic systems. The Head of Frontier AI owns that transition across every customer that builds or adapts models: foundation labs today, and a growing set of tech companies and enterprises over time.

The role

You will own revenue growth from model builders, across foundation labs, large tech companies, and enterprises: expanding existing accounts, opening new ones, and adding new types of revenue on top of the services we sell today. This is a commercial leadership role with a product mandate. You will run these client relationships end to end, and you will be the voice of the customer inside Invisible, working with Product and Engineering to turn what labs need into offerings we can sell at scale.

Your mandate has three parts:

• Grow existing clients by expanding scope, deepening multi-threaded relationships, and moving accounts from one-off projects to recurring programs.

• Win new clients across foundation labs, emerging frontier labs, large tech companies building genAI products, and enterprises training or fine-tuning models for agentic solutions.

• Build new revenue lines by bringing newer offerings (staff aug, data brokerage, agentic trajectories) to market alongside our core RLHF and SFT work.

Who you'll sell to

Segment

Who they are

What they need from us

Foundation and frontier labs

Labs training frontier models

Post-training data, RL environments, agentic trajectories, specialist talent at scale

Large tech companies

Teams building genAI products and in-house models

Domain-specific SFT/RLHF data, evals, embedded experts

Enterprises

Companies moving from buying AI to building or fine-tuning models for agentic workflows

Proprietary-data fine-tuning, evaluation, trajectories for their own agents, staff aug

Labs are the core of the business today; tech companies and enterprises are where the next wave of model-building revenue will come from. Enterprise accounts are run in partnership with Enterprise GTM.

What you'll sell

You will sell a portfolio that spans our established services and the newer, more productized lines we are building.

Offering

What labs buy

Where it is today

RL environments and long-horizon agentic trajectories

Environments, tasks, graders, and multi-step agent trajectories for reinforcement learning

Growth line; shaped closely with Product and Engineering

RLHF / SFT

Expert-generated preference data, demonstrations, and evaluations for post-training

Core revenue; scale and margin expansion

Staff augmentation

Vetted domain experts and specialists embedded with lab teams on W-2 or 1099 terms

New business unit; first deals and repeatable motion

Data brokerage

Licensed and sourced datasets matched to lab training needs

New revenue line; packaging, pricing, and first buyers

Responsibilities

Revenue and growth

• Own the model-builder revenue number across labs, tech companies, and enterprises: bookings, net revenue retention, and new-logo targets.

• Build and run the account plan for each key account, including whitespace mapping across all four offerings.

• Source, scope, price, and close new programs; lead negotiations on MSAs, SOWs, and commercial terms.

• Forecast accurately and run a disciplined pipeline that leadership can plan against.

Client relationships

• Be the senior relationship owner with research leads, post-training and AI platform teams, data leads, and procurement at each account.

• Multi-thread every account so relationships do not depend on a single champion.

• Run executive business reviews and turn delivery results into expansion.

• Escalate and resolve delivery issues before they put renewals at risk.

Product and business transformation

• Bring structured customer insight to Product and Engineering: what each segment is training on next, where data bottlenecks are, and what they will pay for.

• Co-define packaging and pricing for productized offerings, moving revenue from bespoke services toward repeatable, higher-margin products.

• Pilot new revenue lines (staff aug, data brokerage, agentic trajectories) with early-adopter clients and turn pilots into standard offerings.

• Partner with Delivery and Operations to make sure what is sold can be delivered at quality and margin.

Team and market

• Build and lead a team of account executives and engagement leads as the business scales.

• Represent Invisible in the frontier AI community through conferences, research relationships, and thought leadership.

• Track competitors and market pricing, and feed that into positioning and strategy.

What success looks like

First 90 days

• Meet the senior stakeholders at every active key account and have a written account plan for each.

• Own the model-builder pipeline and forecast across all three segments, with a clear view of expansion and new-logo targets.

• Close or advance at least one deal in a new revenue line (staff aug, data brokerage, or agentic trajectories).

• Agree with Product and Engineering on the top customer-driven priorities for the next two quarters.

First year

• Hit the model-builder revenue target, with growth from both existing and new accounts.

• Measurably shift revenue mix toward productized, recurring offerings.

• Land new logos in each segment, including first enterprise model-building programs, and multi-thread every top account.

• Have a hired, ramped team running a repeatable sales motion.

Qualifications

Required

• 10+ years in enterprise sales, business development, or account leadership, including 3+ years leading a team or a major book of business.

• Existing, senior relationships at one or more foundation or frontier AI labs, plus credibility with AI leaders at large tech companies or enterprises.

• A track record of closing and expanding multi-million-dollar, multi-year deals with technical buyers.

• Working fluency in how frontier models are built: pretraining, SFT, RLHF, RL environments, evals, and agentic systems.

• Experience selling a portfolio that blends services and technology, with strong commercial instincts on pricing, packaging, and margin.

• Proven ability to work cross-functionally with Product, Engineering, and Delivery to shape what gets built.

Preferred

• Prior leadership at an AI data, training, or evaluation company, or on the data side of a frontier lab.

• Experience taking a services business toward productized or platform revenue.

• Exposure to staffing, talent marketplaces, or data licensing models.

• Experience selling AI/ML platforms or services into enterprises adopting agentic AI.

• Technical background (CS, ML, or a quantitative field).

Who you are

• Credible with researchers and enterprise buyers alike. You can hold a credible conversation about reward models, eval design, and agent trajectories, not just commercial terms.

• A builder, not just a closer. You see a recurring customer need and push to turn it into a product.

• Comfortable with ambiguity. New offerings will be half-formed when you start selling them; you help shape them.

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