Full-Time
Head of Marketing
Prime Intellect
51-200 employees
Decentralized GPU compute marketplace for AI
Compensation Overview
$150k - $300k/yr
H1B Sponsorship Available
San Francisco, CA, USA
Remote
Remote work option available; relocation sponsorship and visa support provided.
 Growth & Marketing (2) |
|---|
| , |
Get referred to Prime Intellect
Find people who can refer or advise you
- 5+ years in marketing, developer relations, or developer marketing at a B2B infrastructure, open-source, or developer-tools company.
- Deep technical fluency — you can credibly discuss GPUs, RL training loops, model architectures, and API design with engineers and researchers.
- Proven track record building developer communities and running high-impact product launches.
- Strong writer and storyteller who can produce or direct content that resonates with both technical and business audiences.
- Analytical and experiment-driven — comfortable with attribution models, funnel metrics, and growth experiments.
- Experience building small, high-performing teams from scratch in fast-moving environments.
- Define and evolve Prime Intellect's brand positioning, voice, and visual identity across all channels.
- Translate deeply technical capabilities (async RL, distributed training, agentic post-training) into clear, compelling market narratives.
- Own messaging frameworks for product launches, fundraise announcements, research releases, and partnership news.
- Build and lead the DevRel function — creating programs, content, and experiences that drive developer adoption and loyalty.
- Grow and engage our open-source community (Environments Hub, prime-rl, Verifiers) through tutorials, office hours, hackathons, and contributor programs.
- Develop and maintain developer documentation, quickstarts, and onboarding flows in close partnership with Product and Engineering.
- Represent Prime Intellect at conferences, meetups, and on podcasts — and identify opportunities for the founding team to do the same.
- Build a content engine spanning technical blog posts, research digests, case studies, video content, and social media.
- Partner with Research to translate frontier work (Recursive Language Models, INTELLECT model series, CLaaS) into high-signal public content.
- Develop a thought leadership program that positions Prime Intellect's founders and researchers as key voices in open AI infrastructure.
- Plan and execute product launches, research releases, and community events that generate developer awareness and adoption.
- Run growth experiments across channels — organic social, paid, SEO, email, partnerships — and build measurement systems to track what works.
- Own top-of-funnel metrics: developer signups, community engagement, content reach, and brand awareness.
- Partner with Sales/GTM to ensure marketing-generated demand converts into pipeline and design-partner relationships.
- Build and manage the marketing tech stack (analytics, CMS, email, social scheduling, attribution).
- Establish reporting cadence for marketing KPIs and campaign performance.
- Manage external agencies, freelancers, and contractors as needed for design, video, and content production.
- Hands-on experience with AI/ML infrastructure, cloud platforms, or open-source ML tooling.
- Existing presence and credibility in the AI research or infrastructure community (blog, Twitter/X, conference talks).
- Experience with open-source community building and contributor growth programs.
- Early-stage operator mindset — bias for building playbooks, not inheriting them.
Prime Intellect builds a decentralized, peer-to-peer platform for AI development. It operates Prime Intellect Compute, a GPU marketplace that aggregates resources from multiple cloud providers so users can access affordable compute time for AI projects. The Prime Intellect Protocol governs open-source AI with community ownership and governance, enabling anyone to contribute compute, capital, and code for distributed model training. Its goal is to democratize AI development by providing a scalable, marketplace-driven, globally distributed environment for training and deploying advanced models.
Company Size
51-200
Company Stage
Series A
Total Funding
$150.5M
Headquarters
Dover, Delaware
Founded
2024
Get referred to Prime Intellect
Find people who can refer or advise you
Simplify's Take
What believers are saying
- The company serves over 6,000 clients with $100M annualized revenue, showing rapid enterprise adoption.
- Its platform aggregates idle data center compute to address the projected $88.7B reinforcement learning market by 2032.
- Key investors like NVIDIA Ventures and Intel Capital validate its hybrid centralized and decentralized GPU strategy.
What critics are saying
- NVIDIA's Nemotron Coalition and proprietary RL environments directly crowd out Prime Intellect's open post-training stack.
- BrowserEnv's exclusive partnership with Browserbase creates a single-point dependency for browser agent training infrastructure.
- INTELLECT-3's distributed RL may fail to match centralized models' reasoning quality, invalidating Prime's core thesis.
What makes Prime Intellect unique
- Prime Intellect is an asset-light compute broker orchestrating global GPU supply without owning infrastructure.
- It offers a full-stack open superintelligence stack including Prime RL, Verifiers Library, and Environments Hub.
- The Prime Intellect Protocol enables decentralized, peer-to-peer ownership and governance of open-source AI models.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Company Equity
Flexible Work Hours
Remote Work Options
Relocation Assistance
Professional Development Budget
Conference Attendance Budget
Growth & Insights and Company News
6 month growth
↓ -13%1 year growth
↓ -11%2 year growth
↑ 13%Prime Intellect has raised $130 million in a Series A round led by Radical Ventures, with participation from NVIDIA Ventures, Intel Capital, and Dell Technologies Capital. The company, founded by Vincent Weisser and Johannes Hagemann, has built an open platform for AI companies to train, deploy, and improve their models using reinforcement learning (RL). The platform integrates compute, environments, evaluations, RL post-training, and inference capabilities. RL is emerging as a critical method for training AI models as traditional data sources reach saturation. However, running RL on large language models is significantly more complex than standard fine-tuning, requiring multiple models to work together with substantial memory and compute overhead. Prime Intellect's architecture aggregates idle data centre compute globally, enabling RL training across fragmented GPU supply. The RL market was valued at $2.8 billion in 2022 and is projected to reach $88.7 billion by 2032.
Introducing browserenv: train browser agents on real websites. Harsehaj Dhami Growth Engineer Kyle Jeong Growth Engineer March 25, 2026 TL;DR: Browserbase and Prime Intellect have partnered to launch BrowserEnv, a reinforcement learning environment for training and evaluating browser agents on real web tasks. Everyone wants AI models that can actually use the browser to get work done, but most models weren't trained to interact with real websites. They were trained on static datasets instead of environments where they can practice navigating pages, clicking elements, and completing multi-step workflows. This is why many browser agents look impressive in demos but struggle in real-world use. The missing piece is a reliable and scalable training environment. Training browser agents requires significant infrastructure when running browsers at scale, interacting with live websites without getting blocked, resetting sessions between tasks, and verifying results. This is the infrastructure frontier labs are already building. For example, Microsoft trained and evaluated their computer-use model Fara-7B using Browserbase, which required reliable access to real websites and scalable browser environments for evaluation and reinforcement learning workflows. Browserbase, Inc. has partnered with Prime Intellect to make this infrastructure accessible to everyone with BrowserEnv. BrowserEnv is a reinforcement learning environment designed specifically for training browser agents. It runs on Browserbase, which provides scalable browser infrastructure and access to real websites. Prime Intellect provides the training platform. Together, they make it possible to train and evaluate computer-use models on real browser tasks without building the infrastructure yourself. All you need is a dataset of tasks. Researchers and developers can train open models like Qwen or other computer-use models using reinforcement learning, while BrowserEnv handles browser orchestration, task execution, and verification. Training Qwen 3 VL on WebVoyager with BrowserEnv. To validate its stack end to end, Browserbase, Inc. fine-tuned Qwen/Qwen3-VL-8B-Instruct on real WebVoyager tasks using BrowserEnv and Prime Intellect. Browserbase, Inc. plugged the prime/webvoyager-no-anti-bot environment into Prime's RL pipeline, so the model could practice real navigation flows across sites like Amazon, Allrecipes, GitHub, Booking, and more without getting stuck on anti bot walls. BrowserEnv handled browser orchestration on Browserbase, Prime handled rollouts and optimization, and WebVoyager provided a standardized benchmark of 600 filtered tasks. Browserbase, Inc. started from the public WebVoyager environment in the Prime hub, switched it to CUA mode, and pointed it at Qwen3-VL-8B-Instruct. The training run used a relatively small but realistic configuration: 200 steps, batch size 32, 8 rollouts per example, learning rate 1e-4, and an oversampling factor of 2, with modest parallelism. model = "Qwen/Qwen3-VL-8B-Instruct" max_steps = 200 batch_size = 32 rollouts_per_example = 8 learning_rate = 0.0001 oversampling_factor = 2 max_async_level = 2 [sampling] max_tokens = 512 [[env]] id = "prime/webvoyager-no-anti-bot" args = {mode = "cua", viewport_width = 800, viewport_height = 600, keep_recent_screenshots = 2} In this setup, each training step created or reused a Browserbase session, loaded a WebVoyager task, and let Qwen3-VL act through coordinate based CUA primitives while a verifier judged task completion and produced reward signals. Over the course of the run, the model improved on multi step tasks such as searching, filtering, and extracting information from live pages, rather than just static HTML. The output of this training run is a LoRA adapter that can be easily deployed to run on the Prime Intellect platform. This training workflow is reproducible by anyone with access to a Browserbase and Prime Intellect account. You can even start from the same ingredients Browserbase, Inc. used: BrowserEnv on Browserbase, the WebVoyager no anti bot environment in Prime, and an open vision language model like Qwen3-VL. Frontier labs are already training browser agents this way, and now anyone with access to the internet can do the same. BrowserEnv is generally available today, learn more at browserenv.com and start training your own browser agents. Train your own custom modelLearn more
Prime Intellect just launched INTELLECT-2, the first globally distributed reinforcement-learning run of a 32-billion-parameter model, with experts predicting community-trained systems in the 70-100 B range by year-end - a potential counterweight to hyperscaler dominance.
Prime Intellect is building a peer-to-peer protocol for compute and intelligence, enabling collective creation, ownership, and access to sovereign open-source AI. We’re moving beyond centralized AI to empower anyone—from solo GPU operators to global datacenters—to contribute compute, code, or capital and shape the open and decentralized AI ecosystem.
Prime Intellect launches initiative to train open model with decentralized computing.