P

Prime Intellect

Open frontier-model stack for enterprise RL

Founding Solutions Architect

Full-Time
$250k - $320k+ Equity
Mid
San Francisco, CA, USA
HybridFlexible work is remote or San Francisco, with travel to customer sites as needed.
Solution EngineeringPre-Sales Solution Engineering
H1B Sponsorship Available

About the job

Requirements
  • Strong background in machine learning infrastructure, distributed systems, cloud, or GPU computing.
  • Working knowledge of modern training and inference stacks, CUDA/GPU environments, and model-serving patterns.
  • Customer-facing experience in solutions architecture, sales engineering, forward-deployed engineering, or a similar role.
  • Ability to communicate with machine learning engineers and executive stakeholders.
  • Ability to prove claims with real customer workloads and benchmarks.
  • High ownership, including identifying gaps and building solutions proactively.
  • Use of large language models, automation, and programmatic tools in work.
Responsibilities
  • Lead deep technical discovery across training, post-training, and inference workloads.
  • Understand each customer's GPU requirements, cluster topology, networking, storage, utilization patterns, and performance objectives.
  • Design production-ready customer architectures on Prime infrastructure that meet their objectives.
  • Partner with engineering on migration and deployment requirements to ensure designed solutions are shipped.
  • Own workload benchmarks and proofs of concept against customer baselines and competitive alternatives.
  • Translate latency, throughput, utilization, and cost results into a defensible technical business case for machine learning engineers and executive buyers.
  • Set benchmark quality standards for methodology, reproducibility, and honest comparison.
  • Reduce time to technical validation with each deal.
  • Develop repeatable benchmark and reference-architecture playbooks that scale beyond the individual role.
  • Support technical evaluation and adoption of Model Factory, bringing customers from compute onto the full training stack.
  • Feed insights from customer workloads into product and research priorities.
Desired Qualifications
  • Hands-on experience running or optimizing large-scale distributed training (FSDP, Megatron, DeepSpeed, or similar) or high-throughput inference (vLLM, SGLang, TensorRT-LLM).
  • Experience benchmarking across hardware generations (H100, H200, GB200) and interconnects (InfiniBand, RoCE).
  • Background at a GPU cloud, neocloud, hyperscaler AI team, or frontier lab infrastructure team.
  • Experience building a solutions or forward-deployed function from scratch.

About the company

Prime Intellect provides an open, end-to-end superintelligence stack for enterprises to train and continually improve their own AI agents. The platform includes scalable compute, large-scale reinforcement learning, environments, sandboxes, evaluations, and deployment tools, and it ships the same stack to customers while also training open frontier models. It differentiates itself by offering a complete lifecycle solution—from model training to deployment—focused on agentic training and large-scale RL, backed by thousands of customers and strong funding. Its goal is to help organizations build, train, and continuously improve their own AI agents at scale using open frontier models, with ongoing post-training support for customers.

Company Size

51-200

Company Stage

Series A

Total Funding

$150.5M

Headquarters

San Francisco, California

Founded

2024

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Simplify's Take

What believers are saying

  • October 2, 2026 Prime Inference launched with serverless and reserved serving across datacenters.
  • July 8, 2026 Prime Intellect reported over 6,000 customers and $100 million annualized revenue.
  • September 23, 2026 Sandboxes reached general availability with 30 million sandboxes created.

What critics are saying

  • OpenAI, Anthropic, and OpenRouter can compress inference margins faster than Prime Intellect scales.
  • October 2026 Sandboxes and Prime Agent execute customer code, creating breach and liability exposure.
  • If enterprise RL commoditizes, Prime Intellect becomes a GPU reseller with fragile differentiation.

What makes Prime Intellect unique

  • July 8, 2026 Series A valued Prime Intellect at $1 billion with Radical Ventures.
  • Prime Intellect bundles compute, RL, sandboxes, evaluations, inference, and deployment into one stack.
  • October 2, 2026 Prime Inference and September 23 Sandboxes deepen its closed training loop.

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Benefits

  • Company Equity
  • Flexible Work Hours
  • Remote Work Options
  • Relocation Assistance
  • Professional Development Budget
  • Conference Attendance Budget

Growth & Insights and Company News

Headcount

6 month growth

8%

1 year growth

0%

2 year growth

26%
AlphaSignal
Oct 13th, 2026
Prime Intellect buys Moondream to double vision AI throughput.

Prime Intellect buys Moondream to double vision AI throughput. The neocloud is folding Moondream's open vision models, Photon inference engine, and megakernel compiler into its stack to chase cheaper multimodal inference. · 1 hr ago Read 5 min * Prime Intellect acquired Moondream, bringing open vision models and the Photon inference engine in-house. * Moondream founders Vik Korrapat and Jay Allen join to lead inference and multimodal research. * Financial terms undisclosed; Moondream had under 10 staff and $4.5M raised previously. * Photon claims roughly 2x throughput over vLLM, with 34ms end-to-end inference on a B200. * Moondream 3.1, Clef, and Clef Flash are already live on Prime Inference and OpenRouter. * Deal targets underserved robotics and computer-use agent markets on open models. Prime Intellect buys Moondream to sharpen AI inference. Prime Intellect, a San Francisco AI infrastructure provider valued above $1 billion, has acquired Moondream, the small open-source lab behind a widely downloaded family of vision-language models. The deal brings Moondream's open weights, Photon inference engine, and proprietary GPU compiler into Prime Intellect's training and serving platform. The acquisition gives Prime Intellect technology for running trained models with lower latency and higher throughput. Those gains can reduce serving costs and improve responsiveness in robotics, video processing, and computer-use agents, where models must interpret a stream of images or screen states. Founders join; terms stay private. Moondream employed fewer than 10 people and had raised $4.5 million before the acquisition. Founders Vik Korrapat and Jay Allen will join Prime Intellect to lead inference and multimodal research. | Deal detail | Status | | Purchase price | Not disclosed | | Closing timeline | Not disclosed | | Founders | Joining Prime Intellect | | Existing model licenses | Unchanged, according to the companies | | Integration plan | Photon and Moondream research will join Prime Intellect's platform | Founded in January 2024, Prime Intellect announced a $130 million Series A on July 8, 2026, bringing its reported funding above $150 million. The company also reported more than $100 million in annualized revenue that month. CEO Vincent Weißer said the acquisition would support expansion into robotics and computer-use agents, markets that often require fast visual inference. Photon targets the serving bottleneck. Moondream's public profile comes from compact vision-language models that collectively exceed 5 million monthly downloads. Photon is the serving engine beneath those models, handling the work required to schedule requests, process images, and execute operations on a GPU. Photon uses model-specific scheduling, native image processing, and custom CUDA kernels. Prime Intellect reported the following results for Moondream workloads: | Hardware or comparison | Reported result | | vLLM comparison | About twice the throughput on comparable workloads | | NVIDIA B200 | 34 milliseconds of end-to-end inference | | NVIDIA H100 | More than 60 inferences per second | These figures come from the companies' announcement. Production performance will depend on model size, precision, batch size, image resolution, request patterns, and hardware configuration, so teams should reproduce the tests with their own workloads. Photon's compiler generates model-specific megakernels, which combine several GPU operations into a larger unit of work. This reduces kernel-launch overhead and intermediate memory transfers, keeping more of the GPU occupied during inference. Automating that process can shorten the optimization work required for each model, which otherwise involves extensive manual tuning by CUDA engineers. The integration is already live. Prime Intellect had integrated part of the acquired technology by the time it announced the deal. Moondream 3.1 is available through Prime Inference with Photon handling execution. Clef and Clef Flash are also available through Prime Inference and OpenRouter. Prime Intellect describes the product plan in three parts: * Moondream: Continue developing compact multimodal models under the existing brand. * Photon: Extend the runtime and kernel work to more open models and input types. * Prime Inference: Add support for video, voice, decision models, robotics, and computer-use agents. Custom runtimes squeeze more from each GPU. Neoclouds rent GPU capacity and offer managed infrastructure for training or serving AI models. Inference providers occupy a related market, giving application developers hosted access to open models without requiring them to operate GPU clusters or serving software. Falling per-token prices have increased pressure on these providers to improve throughput, latency, utilization, and reliability. Widely used runtimes such as vLLM and SGLang provide strong general-purpose baselines. A custom runtime can improve margins when it serves more requests from the same hardware, provided those gains hold across real production traffic. The strategy resembles Modular's acquisition of BentoML earlier in 2026. Both deals combine model-serving products with lower-level compiler or runtime expertise, giving the acquiring company more control over the path from an API request to GPU execution. What developers should verify. Developers considering Moondream or Photon should evaluate the acquisition through several practical checks: * Benchmark representative traffic. Test the expected image sizes, batch patterns, concurrency, precision, and latency targets instead of relying on headline throughput. * Confirm hardware coverage. Prime Intellect says the first Photon release supports Moondream, Qwen, and Gemma on NVIDIA H100 GPUs. Support for additional accelerators and deployment environments remains unspecified. * Review each license. Prime Intellect says Moondream's existing licensing remains unchanged. The compiler is described as proprietary, and the announcement does not provide broader access terms for Photon. * Check deployment options. Teams with edge or on-premises requirements should confirm whether Photon can run outside Prime Intellect's hosted platform. * Track release continuity. Open-weight users should watch model updates, repository activity, compatibility guarantees, and deprecation policies as integration proceeds. The acquisition gives developers immediate hosted access to Moondream models and a runtime designed for efficient multimodal inference. Photon's portability, reproducible performance across broader workloads, hardware support, and long-term release policy remain the key details to watch.

Crypto Briefing
Oct 13th, 2026
Prime Intellect acquires Moondream to speed up AI inference

Prime Intellect acquires Moondream to speed up AI inference. The $1 billion AI infrastructure startup is buying a tiny open-source shop for its GPU optimization tools, not its vision model 3 hours ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera Prime Intellect, the San Francisco AI infrastructure startup, is acquiring Moondream, an open-source multimodal AI company. The goal is faster AI inference at a lower computing cost. The deal was announced on October 13, 2026. On paper it's a small company buying an even smaller one. In practice, it's a bet that the next fight in AI is about running models efficiently, not just building bigger ones. What Prime Intellect is actually buying. Moondream is best known for its multimodal vision-language model, the kind of AI that can look at an image and talk about it. That model isn't the main attraction here. Prime Intellect is after Moondream's inference optimization software. That includes tools that can generate what are called megakernels automatically. The acquisition targets one use case in particular: running open models on NVIDIA GPUs and servers. Open models can be downloaded and run by anyone. Making them cheaper to operate helps the developers and businesses that rely on them instead of closed, API-only systems. Moondream brings a small team to the table. The company has fewer than 10 employees and had previously raised $4.5 million in funding. Neither company has disclosed the financial terms of the acquisition. No closing timeline has been shared, and integration plans have not been made public either. A fast climb for the buyer. Prime Intellect has not been around long. The company was founded in January 2024, which in startup terms makes it barely out of the onboarding phase. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Its growth has been quick. By July 2026, Prime Intellect reported an annualized revenue run rate exceeding $100 million. That same month, the company closed a $130 million Series A round at a $1 billion valuation. CEO Vincent Weißer framed the deal as a way to reach markets he sees as underserved. He pointed specifically to robotics and computer-operating agents, the AI systems designed to use software and navigate interfaces the way a person would. Why inference efficiency matters now. Training a model gets the headlines. Inference is where the bills keep arriving. Training happens once, or at least occasionally. Inference happens every time someone asks a model a question, every time a robot processes a camera frame, and every time an agent clicks through a webpage. Those costs scale with usage, and they never really stop. The robotics and agent markets Weißer highlighted make this even more pressing. A chatbot that takes an extra second to respond is mildly annoying. A robot that takes an extra second to react to its surroundings is a different problem entirely, and an agent that lags on every click quickly becomes tedious to use. Moondream's background in vision-language models may also fit neatly here. Robots and computer-operating agents both depend on interpreting visual input, whether that's a warehouse floor or a desktop screen. What this means for the AI infrastructure race. There are open questions worth tracking. With no financial terms disclosed, it's impossible to judge how much Prime Intellect paid relative to its own $1 billion valuation. The lack of a public integration plan also means it's unclear how quickly Moondream's tools will show up in Prime Intellect's products. The status of Moondream's own open-source vision-language model is another variable. The deal centers on the optimization software, so developers who rely on the model will likely watch for any signals about its future maintenance. Disclosure: This article was edited by Diego Almada Lopez. For more information on how Cryptobriefing create and review content, see its Editorial Policy.

The Information
Oct 13th, 2026
Prime Intellect, valued at $1B, buys Moondream to speed up open model inference

Prime Intellect, a neocloud valued at $1 billion in an equity financing in July, is acquiring Moondream, a startup with fewer than 10 employees. The San Francisco company wants to make open models run faster and more cheaply on its Nvidia servers than its rivals do. Chief executive officer and co-founder Vincent Weisser declined to share the deal terms. Moondream has raised $4.5 million in funding. Moondream, based in San Francisco and Seattle, is best known for an open-source vision-language model that analyses images, such as identifying boxes in a warehouse photo or ads in a livestream. Weisser said Prime Intellect is largely buying the startup for its employees' skills in writing software that makes models run more efficiently.

Prime Intellect
Oct 13th, 2026
Moondream is joining Prime Intellect.

Moondream is joining Prime Intellect. Prime Intellect, Inc. has acquired Moondream. Founders Vik Korrapati and Jay Allen are joining Prime Intellect to lead and scale inference, and to accelerate its work on multimodal models, including vision and computer use. Moondream was one of its earliest customers, and Prime Intellect, Inc. has long admired what Vik and Jay built with a tiny team. They've shipped state-of-the-art open-weight vision-language models with millions of monthly downloads.[1] They also built Photon, an inference engine that compiles each model into a single GPU program, along with custom GPU kernels and the megakernel compiler behind them.[2] "Moondream is proof that small, efficient models can beat giant ones at specific tasks. Prime is building for that same future: many specialized models that companies own. We were Prime customers long before we joined. Now Photon can power far more than our models, and our models get Prime's full training stack." co-founder of Moondream Moondream 3.1 leads on object detection, aerial images, charts, and crowded scenes like store shelves. It does this at 34 requests per second, about 24x faster than the next most accurate open model.[3] Compared with Meta's new frontier model Muse Wonder, Moondream 3.1 reaches 80% of its accuracy at pointing at objects in photos, at about 1/1000th of the cost.[4] Much like Prime Intellect, Moondream made the bet that a model and its underlying infrastructure have to be built together, and that a specialized, efficient model running on the right infrastructure beats a generalist at a given task. Prime Intellect, Inc. is excited to double down on this together and help everyone train, deploy and serve frontier multimodal models across video, image, voice and computer use. Why inference. Prime Intellect, Inc. launched Prime Inference because serving is where a model's usefulness gets decided: inference dictates how fast a model responds and what it costs to run.[5] This is where Moondream's systems work pays off: answers come back in tens of milliseconds, often 40x+ faster than calling a frontier API. On a single H100, Photon runs more than 60 Moondream inferences per second, which is fast enough for live video.[6] Put together, Moondream's models get Prime's full training stack, Photon gets to power far more than Moondream's models, and Prime customers get a multimodal stack that was built as one system rather than assembled from parts. Photon and Moondream's kernel work are being integrated into Prime's stack. It is a commitment to having the fastest inference on the market and making its customers run models as well as they train them. Building together. Moondream built exceptional models and a specialized inference engine. Prime's open superintelligence stack connects evaluation, fine-tuning, reinforcement learning and inference. Together, Prime Intellect, Inc. combine its training platform with efficient serving and bring on Vik and Jay's deep expertise in vision and computer use. Prime Intellect, Inc. has already started shipping. Moondream 3.1 is now available through Prime Inference and powered by Moondream's Photon engine. Clef and Clef Flash are also live through Prime Inference and OpenRouter, with Clef priced at parity with Jev and Clef Flash at half the price.[7] In the next few days, every customer of Moondream Cloud will receive credits on its platform, usable for Moondream models and its broader training and inference offerings. Moondream will remain its brand for small, efficient multimodal models. Its mission is to make open superintelligence accessible to every company. If you're building vision models, computer-use agents or need the fastest inference on the market, Prime Intellect, Inc.'d love to work with you. References. * Korrapati, Vikhyat. "moondream2." Hugging Face. Accessed October 13, 2026. | * Moondream. "Photon: Official Moondream Inference Engine." Accessed October 13, 2026. | * Moondream. "Moondream 3.1: Beyond Benchmarks." Moondream Blog, 2026. | * Prime Intellect. "Pointing Accuracy and Cost per 1,000 Images: Moondream 3.1 and Frontier Models." Unpublished internal evaluation, October 2026. | * Prime Intellect. "Prime Inference: Fast, Reliable Serving for Frontier Open Models." Prime Intellect Blog, 2026. | * Moondream. "Photon Serving Benchmarks, NVIDIA H100." Unpublished internal measurements, October 2026. | * Vigliarolo, Brandon. "Cloudflare Tries to Outplay Jev with Open-Weight Clef Models." The Register, October 1, 2026.

MarkTechPost
Oct 3rd, 2026
Prime Intellect launches Prime Inference: serverless and reserved serving for frontier open models.

Prime Intellect launches Prime Inference: serverless and reserved serving for frontier open models. October 2, 2026 Prime Intellect has launched Prime Inference, a serving platform for frontier open-source models. It offers serverless endpoints and reserved capacity on Prime's own GPUs across multiple datacenters. Before public release, it processed nearly a trillion tokens per day internally. That traffic came from RL rollouts, synthetic data generation, evaluations and long-running coding agents. What is Prime Inference? Prime Inference is the serving layer of Prime Intellect's open training stack. The company already ships post-training tools such as prime-rl, verifiers and sandboxes. Serving closes that loop: deployed models generate production traces that can feed back into training. Prime reports its GLM-5.3 endpoint ranks among the fastest on OpenRouter. It also cites a near-zero tool-call error rate and 100% uptime since launch. * Two modes: serverless endpoints for variable demand, reserved capacity for sustained workloads. * OpenAI compatible: point any OpenAI SDK at https://api.pinference.ai/api/v1 (docs). * Uptime: automatic failover across datacenters routes traffic to healthy deployments. * Hardware: NVIDIA Blackwell today, with Vera Rubin listed as coming soon. * Billing: unified billing with team-level usage tracking. Per-model pricing is not yet fully published in the docs. How the serving stack works. The stack combines NVIDIA Dynamo, vLLM, Mooncake and FlashInfer. It was built with Inferact and NVIDIA, and fixes are contributed upstream. The target workload is agentic. A typical agent turn adds about 6K tokens to a 140K-token prompt. Prime benchmarks this mix with SemiAnalysis AgentX, and injected cold arrivals. Prefill/decode disaggregation: Prefill and decode run on separate GPU groups. Dynamo handles routing, and vLLM runs the model on each group. Decoders pull computed KV through NIXL. Prime reports nearly 40% lower p90 inter-token latency in its tests. Cache-aware routing:Dynamo's KV-aware router weighs cached prefix overlap against queued work. Sessions stay on the same decoder between turns. Mooncake adds a second KV tier in host DRAM. GLM-5.3 on GB200 NVL72: the numbers. The interactivity target was 100 end-to-end tokens per second per user. At that bar, a 1:4 prefill/decode ratio served the most users. It reached 66 sessions per prefill group at 101 tok/s per user and 100 output tok/s per GPU. * DEP8 prefill topology: roughly 5x more usable prefix-cache capacity than TEP8 on the same hardware. * Smaller prefill budget: halving tokens per step from 8K to 4K per GPU cut median queue wait from 550 ms to 110 ms. Median time to first token fell about 20%. * NVFP4 KV compression: each MLA cache row shrank from 576 to 352 bytes. Cached tokens per decoder rose from 1.09M to 1.63M. * Native sparse-MLA kernel: about 12.0 μs at 15 query tokens, versus 17.7 μs staged and 13.7 μs FP8. Prime notes this is workload specific. * BLHNC KV layout: transfer descriptors fell from 19,559 to about 1,940. Mean transfer time dropped from 146 ms to 78 ms. Reliable tool calls. Agents fail when tool calls carry wrong names or broken arguments. Prime Intellect's team contributed a structural-tag builder to Dynamo for GLM's tool format. vLLM then uses xgrammar to mask tokens that violate the tool schema. The team also fixed parsing bugs, including < being decoded into < inside code. Interactive explainer. Prime Inference vs closest competitors. Competitor prices verified October 2, 2026. Tracker figures come from ComputePrices, a third-party price tracker. Key takeaways. * Prime Inference is live with serverless and reserved serving for open models. * GLM-5.3 runs on GB200 NVL72 with Dynamo, vLLM, Mooncake and FlashInfer. * 1:4 prefill/decode served 66 sessions per prefill group at 101 tok/s per user. * NVFP4 KV cache lifted capacity from 1.09M to 1.63M tokens per decoder. * Batch inference and 1-click dedicated deploys are next on the roadmap. Need to partner with Marktechpost LLC. for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with Marktechpost LLC. Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.