Prime Intellect · San Francisco
Sales & BusinessGrowthFullTimeposted 2mo ago
Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.
Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.
The most important AI products of the next decade will not be built by simply renting GPUs or calling an API.
They will be built by teams that can define the right tasks, construct the right environments, measure the right outcomes, run the right post-training loops, and deploy models that improve on real workflows.
Prime Intellect gives customers that capability. Your job is to make it real.
As a Forward Deployed AI Strategy Lead, you will work directly with strategic customers to identify high-value AI workflows, translate them into evals and post-training opportunities, scope technical deployments with Applied Research, and turn early experiments into long-term revenue.
You are part customer owner, part product strategist, part AI systems thinker, and part commercial operator.
You will not sit between the customer and the technical team as a messenger. You will sit with both sides and help invent the answer.
You will lead high-priority customer workstreams from first technical discovery through POC, deployment, expansion, and case study.
You will work with customers who are trying to build agents, automate complex workflows, improve model performance, reduce inference cost, build domain-specific evals, or run frontier-scale post-training.
You will help them answer:
What should we train or evaluate?
What does success actually mean?
What workflows are worth turning into environments?
What data or traces are needed?
What should be automated, supervised, or measured?
Which model should be adapted?
What is the path from prototype to production?
Customers rarely arrive with a perfectly defined problem.
You will take messy conversations, scattered artifacts, internal docs, product goals, and technical constraints, and turn them into crisp scopes that Applied Research and Engineering can actually execute.
You will define:
Use cases
Success metrics
Eval design
Environment requirements
Integration needs
Milestones
Commercial structure
Risks and dependencies
Expansion path
This role works hand-in-hand with Applied Research.
You will bring customer signal into the research and product roadmap, helping the team identify which evals, environments, agents, and post-training recipes matter most in the field.
You will help prioritize work that can both advance the frontier and unlock meaningful customer outcomes.
You should be excited to spend time around questions like:
How do we convert real-world workflows into reliable RL environments?
What makes an eval useful instead of decorative?
When is a verifier good enough?
What makes a task trainable?
Where does managed RL outperform prompting or manual workflow design?
How do we prove performance improvement to a skeptical customer?
Every strategic deployment should make the next one easier.
You will help build the operating system for Prime Intellect’s applied AI motion:
Discovery templates
Customer qualification frameworks
POC structures
Proposal language
Pricing and packaging inputs
Reference architectures
Case studies
Technical narratives
Deployment playbooks
You will help turn one-off customer wins into a repeatable category.
This is a customer-facing role with real revenue responsibility.
You will work with leadership to move customers through qualification, legal, scoping, proposal, procurement, POC, deployment, and expansion.
You should be comfortable owning senior customer relationships, creating urgency, writing crisp follow-ups, navigating internal and external stakeholders, and making sure important deals do not die in ambiguity.
We are looking for people who are unusually strong across technical understanding, customer empathy, product judgment, and execution.
You might be a strong fit if you have experience in:
Forward deployed engineering or technical GTM
AI product strategy or applied AI
Solutions architecture for highly technical products
Early-stage startup operating roles
Product management for AI, infra, devtools, or enterprise software
ML engineering, applied research, or AI engineering with customer exposure
Venture/investing roles with deep technical and commercial work in AI
You should have:
Strong intuition for AI products and workflows
Ability to understand technical systems without needing every detail pre-digested
Excellent written and verbal communication
Comfort operating with executives, researchers, engineers, and operators
High agency and low ego
Ability to run multiple complex customer workstreams
Taste for what makes a deployment valuable
Strong commercial instincts
Deep curiosity about post-training, agents, evals, RL, and AI infrastructure
Ability to make progress before the playbook exists
Experience with RL, SFT, evals, agents, MCP, LangGraph, DSPy, Stagehand, Browserbase, or tool-use workflows
Experience working with enterprise AI teams or frontier AI companies
Ability to read traces, product docs, API docs, or technical specs and turn them into a deployment plan
Experience writing proposals, customer memos, technical scopes, or launch narratives
Founder or early startup experience
Strong network across AI startups, research labs, or enterprise software buyers
This is one of the highest-leverage roles at Prime Intellect.
The frontier is moving from models to systems: agents, environments, evals, training loops, and deployment infrastructure. Most companies know they need to adapt models to their own workflows, but they do not know how to turn that ambition into a working system.
You will be the person who helps them get there.
You will work on customer problems that are technically real, commercially urgent, and strategically important. You will help shape the product, close the revenue, and define the emerging category of full-stack post-training infrastructure.
This is a role for builders who want to be close to the frontier and close to the market.
Competitive cash compensation and meaningful equity
Flexible work in San Francisco or hybrid-remote
Visa sponsorship and relocation support
Professional development budget
Team off-sites and conference attendance
Direct exposure to frontier AI labs, leading AI startups, and enterprise AI teams
A rare opportunity to help define how the next generation of AI systems are trained, evaluated, and deployed
Apply to help Prime Intellect turn ambitious customer workflows into post-training systems, revenue, and the foundation for open superintelligence.