Forward Deployed AI Strategy Lead
Prime Intellect AI
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Benefits offered by this job
Job summary
Prime Intellect AI seeks a Forward Deployed AI Strategy Lead to drive high-value AI workflows with strategic customers. You will translate customer needs into evals, post-training opportunities, and scalable deployments.
You will work across Applied Research, Engineering, and commercial teams to define use cases, metrics, and environments, shaping the roadmap for a reusable post-training infrastructure. Strong communication and risk management are essential.
Qualifications
- Strong mix of technical understanding, customer empathy, product judgment, and execution.
- Experience in forward deployed engineering or technical GTM.
- Background in AI product strategy or applied AI.
- Solutions architecture for highly technical products.
- Experience in early-stage startup operating roles.
Responsibilities
- Lead high-priority customer workstreams from discovery through deployment and case study.
- Define use cases, success metrics, eval design, and environment requirements.
- Partner with Applied Research to align customer signals with roadmap.
- Build repeatable processes: POC structures, pricing, and reference architectures.
- Drive revenue by guiding customers through qualification, legal, scoping, and deployment.
- Communicate clearly with executives, researchers, engineers, and operators.
Skills
Tools
Job description
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
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