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About This Role
Applied AI Product Strategy \& Revenue Lead
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Own Your Intelligence
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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 Role
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This is not a traditional sales role. It is not a traditional product role. It is not a traditional solutions engineering role.
You will help define how Prime Intellect turns frontier post\-training infrastructure into a product customers can understand, buy, deploy, and expand.
Today, the hardest part of the business is not selling raw compute. It is refining the product, customer motion, and technical wedge together with Applied Research, Product, Engineering, and the customer. We are selling something much more complex and much more valuable than GPUs: the ability for customers to build their own lab — environments, evals, verifiers, agents, training runs, and deployment loops that compound over time.
You will own that messy middle.
You will work directly with customers, the CEO, GTM leadership, Applied Research, and Engineering to translate ambiguous customer pain into a concrete product strategy, technical scope, commercial proposal, and path to revenue. You will help us figure out where the product is ready, where it needs to be shaped, what the customer actually wants, and how to turn early traction into repeatable motion.
This is a role for someone who wants to be in the room where a new category is being created.
What You’ll Own
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### Customer\-to\-Product Translation
You will work with frontier AI labs, fast\-growing AI startups, and enterprise AI teams to understand what they are trying to build, where their current stack breaks, and how Prime Intellect can become the infrastructure layer underneath their post\-training and agent workflows.
You will turn vague, high\-stakes customer conversations into clear technical and commercial strategy:
- What is the customer actually trying to improve?
- Is the wedge compute, evals, environments, sandboxes, managed RL, SFT, inference, or a full\-stack workflow?
- What should Applied Research build or prototype?
- What needs to be packaged as product?
- What should be in scope for a POC versus a long\-term deployment?
- What is the fastest path to a strong yes?
### Pre\-PMF Product Strategy
You will help shape Prime Intellect’s product motion before every part of the playbook is obvious.
That means identifying patterns across customer conversations, building repeatable narratives, defining packaging, sharpening use cases, and helping the team understand which customer asks are one\-off noise versus signs of a massive market.
You will help answer questions like:
- How do we explain Lab to different customer segments?
- Which customer workflows should become reference architectures?
- What should we productize versus deliver as managed work?
- Where is the strongest wedge for enterprise customers?
- Which signals show that a customer is ready for managed post\-training?
- How do we turn Applied Research work into revenue without diluting the research agenda?
### Revenue Ownership
You will own high\-value customer opportunities from first serious conversation through qualification, scoping, proposal, POC, procurement, and expansion.
You will not be measured on activity. You will be measured on whether the most important customers move.
This includes:
- Running discovery with technical and executive stakeholders
- Building the business case and technical wedge
- Owning account strategy with leadership
- Drafting proposals, scopes, and commercial structures
- Coordinating internal workstreams across Applied Research, Product, Engineering, Legal, and Finance
- Creating momentum through ambiguity
- Turning early deployments into expansion and long\-term platform revenue
### Applied Research Partnership
You will work extremely closely with Applied Research.
The best version of this role has enough technical taste to understand where an RL/post\-training workflow is real, where a customer is hand\-waving, and where a sharp Applied Research prototype could unlock a major deal.
You will help Applied Research prioritize customer\-facing work by bringing signal from the field:
- Which evals matter?
- Which environments should we build?
- Which agents or workflows are most commercially valuable?
- Which technical demos will change the customer’s mind?
- Which customer problems are actually research problems in disguise?
### Category Creation
The market understands compute. It does not yet fully understand full\-stack post\-training infrastructure.
You will help write the playbook.
You will contribute to positioning, sales narratives, customer decks, case studies, reference architectures, launch moments, and internal strategy. You should be able to turn raw customer conversations into crisp language the entire company can use.
What We’re Looking For
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We are looking for exceptional generalists with rare taste across AI, product, customers, and commercial strategy.
You might come from:
- Technical GTM at a frontier AI, infra, devtools, or enterprise software company
- Founder or early operator experience at an AI startup
- Product or strategy at a highly technical company
- Forward\-deployed engineering, solutions, or applied AI work
- Investing, venture, or strategic finance with deep AI infrastructure exposure
- Research\-adjacent roles where you worked directly with customers or product teams
You should have:
- Strong product and commercial judgment
- Ability to understand technical products quickly
- Excellent written communication
- High agency and comfort with ambiguity
- Taste for what makes a customer problem real
- Ability to work with researchers, engineers, executives, and operators
- Sharp instincts around enterprise buying, POCs, procurement, and expansion
- Obsession with AI, post\-training, agents, evals, and infrastructure
- Ability to create structure where none exists
You do not need to be a researcher, but you should be technical enough to earn trust with researchers and customers.
You do not need to be a traditional salesperson, but you should be commercially intense enough to close.
You do not need to be a PM, but you should have strong product taste.
Bonus Points
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- Experience with RL, SFT, evals, agent frameworks, or LLM post\-training
- Experience selling or deploying infrastructure, AI platforms, devtools, or enterprise AI products
- Experience working with frontier AI labs, model companies, or infra\-heavy startups
- Ability to write excellent customer\-facing decks, memos, proposals, and launch narratives
- Strong network across AI startups, research labs, or enterprise AI teams
- Founder mentality and willingness to do unglamorous work to win
Why This Role
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Most GTM roles ask you to sell a product someone else already defined.
This role asks you to help define the product, the market, the motion, and the revenue engine at the same time.
You will work on the hardest commercial and product questions at one of the fastest\-growing companies in AI infrastructure. You will sit close to customers building real AI systems, researchers pushing the frontier of post\-training, and leadership making company\-defining decisions.
If you want a clean playbook, this is not the role.
If you want to help invent the playbook for how frontier AI infrastructure gets built, packaged, sold, deployed, and scaled, this is the role.
What We Offer
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- 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
- A front\-row seat to building the infrastructure layer for open AI
Ready to Build the Commercial Engine for Open Superintelligence?
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Apply to help Prime Intellect turn frontier post\-training infrastructure into the product, platform, and customer motion that powers the next generation of AI systems.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Prime Intellect, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Prime Intellect AI Hiring
Prime Intellect has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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