Scaled Sales Lead, Beneficial Deployments

1 нед. назад

USALead

Lead go-to-market efforts for scaling beneficial AI deployments at under-resourced organizations such as nonprofits, education institutions, and scientific researchers.

Обязанности

’s Beneficial Deployments accelerates the work of organizations doing some of the world's most important under-resourced work: nonprofits, education institutions, scientific researchers, and organizations advancing economic mobility. Millions of organizations qualify, but only a small fraction will ever talk to a salesperson. All of them face the same problem: mission-critical work, structurally under-resourced, and very little slack to figure out new technology on their own. That gap is what this role exists to close.
You will serve these audiences whether or not a given organization ever generates revenue. Reach comes first, and commercial results follow from it. That ordering is deliberate, and it is the defining feature of the job: a nonprofit, school, or lab that adopts Claude to drive more of its mission is a win on its own terms, and you'll be measured on creating that value at scale. Where an organization does show real depth of adoption, the systems you build route it to the right direct sales team.
You are the go-to-market lead for the scaled Beneficial Deployments ecosystem, specifically the full PLG sales experience  around it: the offers themselves, the skills and plugins these organizations rely on, the ecosystem partners who extend our reach, and the funnel that carries an organization from first signup to real adoption. You think about these audiences as one ecosystem rather than a list of accounts, and you are the single accountable owner for the longtail's experience, funnel, and results globally.
You'll serve it one-to-many: programs, partnerships, and community and lifecycle motions that reach organizations at a scale direct selling can't.
You are also half commercial thinker, half builder. Much of this motion will run as automation you design yourself — deployed, verifiable agentic workflows for activation, education, and expansion: specifying them with domain experts and our applied AI teams, building the evals, and running the feedback loop that improves them over time. The closest existing discipline is GTM engineering, specifically its activation half: you build the plays that reach and move organizations, on top of a data foundation the product team owns. What's unusual is the seat — you own the whole ecosystem's strategy, not a queue of requests.
You'll work in lockstep with the product manager for this space, who owns the purchase path, the program mechanics, and the systems and instrumentation underneath. You own the longtail across every scaled audience: its strategy, its community, its programs, and its results.
This role does not have direct reports today. It may grow into a people-management role over time.
Own the PLG strategy end to end for organizations outside direct sales coverage — across nonprofits, education, sciences, and economic mobility — as one ecosystem, one community, and one market.
Own the longtail experience and funnel: from first signup through activation to expansion, for every audience our giveaway and discount motion reaches.
Lead ecosystem programs across partnerships, cohorts, enablement, content, and the skills and plugins ecosystem — programs that create reach and value independent of revenue.
Run the growth motion — activation playbooks that get a new organization to a working deployment, signal digests and expansion nudges, cohort programs, community-led growth. You set the qualification criteria that route the strongest organizations to the direct sales teams.
Spec, prototype, and deploy the agentic workflows this motion runs on, alongside product and engineering. Define the rubrics and evals, and run the learning loop that makes them better.
Own the ecosystem's numbers — reach, activation, and expansion against goals — and use them to decide where the effort goes next.
Be these sectors' field voice inside : bring deployment learnings and unmet needs to the product teams that own the roadmap for this space.

Требования

Eval construction. You have built evaluations for AI systems — defined the rubrics, measured against them, and used the results to improve a deployed workflow. This is hands-on experience, not oversight of someone else doing it.
Sales leadership. You have led a sales or go-to-market business end to end and been the accountable owner for its results.
Product management. You have owned a product or product surface — set the direction, made the tradeoffs, and shipped it with engineering.

Будет плюсом

Product-led or scaled one-to-many go-to-market experience.
Track record building and running GTM automation, with fluency in agentic workflow design.
Experience growing a community or ecosystem — nonprofit, education, developer, partner, or customer.
Hands-on build comfort: tools like Clay, n8n, SQL, and a willingness to prototype yourself rather than wait for a queue.
An impact, education, research, or mission-driven go-to-market background, and genuine interest in these sectors' constraints.
Founder or zero-to-one go-to-market experience.
Community, developer-relations, or partner-program leadership at scale.
A daily working habit with Claude, and agentic workflows you've built for yourself.

Другое

’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
You want your work measured in organizations doing more of their mission, not only in pipeline.
You'd rather ship a rough workflow this week and improve it from real usage than write the strategy deck first.
You're comfortable being the only person accountable for an ecosystem while depending on teams you don't manage.
You find the constraint interesting rather than depressing: many of these organizations have no budget, no IT department, and no time.
You've been the person who built the thing yourself, because waiting for a queue would have killed it.
 
The annual compensation range for this role is listed below. 
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that recruiters only contact you
We believe that the highest-impact AI research will be big science. At we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to , including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage:  Learn about  our policy for using AI in our application process.