Forward Deployed AI Strategy Lead

Forward Deployed AI Strategy Lead

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, Semi Analysis, Lang Chain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, Open Router, 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 work streams 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…