Head of Frontier AI
Invisible Technologies
Remote · San Francisco, CA · Posted October 17, 2026
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Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
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About the job
About Invisible
Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.
Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.
Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.
Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.
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+ y