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About This Role
Forward Deployed AI Strategy 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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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.
What You’ll Do
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### Own Strategic Customer Deployments
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?
### Turn Ambiguity Into Scope
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
### Partner Deeply With Applied Research
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?
### Build the Repeatable Motion
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.
### Drive Revenue
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.
What We’re Looking For
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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
Bonus Points
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- 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
Why This Role
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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.
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
- 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
Ready to Build the Interface Between Frontier AI and the Real World?
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Apply to help Prime Intellect turn ambitious customer workflows into post\-training systems, revenue, and the foundation for open superintelligence.
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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