Interested in this AI/ML Engineer role at CoreWeave, Inc?
Apply Now →About This Role
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025\. Learn more at www.coreweave.com.
What You’ll Do:
CoreWeave's Product Marketing team sits at the intersection of the world's most advanced AI infrastructure and the customers building on top of it. We are a small, high\-impact team responsible for translating the depth and differentiation of CoreWeave's platform — from bare metal GPU compute to storage, networking, and developer tooling — into narratives that resonate with AI researchers, infrastructure architects, and enterprise decision\-makers alike.
We work closely with Product, Engineering, Sales, and Field Engineering to shape how the market understands CoreWeave: not just as a cloud provider, but as the purpose\-built platform that the world's leading AI labs trust to train and deploy frontier models. This is a team that moves fast, operates with a high degree of autonomy, and holds itself accountable to outcomes — deal velocity, platform adoption, and competitive win rates — not just outputs.
As AI Infrastructure Product Marketing Manager, you will own the end\-to\-end product marketing motion for CoreWeave's infrastructure portfolio: compute, storage, and networking. You'll develop the messaging and positioning that drives pipeline, enables the field, and shapes analyst and media perception in one of the fastest\-growing markets in enterprise tech.
About the Role:
As AI Infrastructure product marketing manager, you will be responsible for defining the value proposition, positioning, messaging, and GTM strategy for CoreWeave’s AI cloud infrastructure. CoreWeave offers a vast portfolio of highly performant AI infrastructure, storage and networking which provides the foundational building blocks for some of the world’s most demanding AI workloads.
This role is at the forefront of our purpose\-built platform for AI including generative AI, agentic, artificial generative intelligence, and beyond. You will work with key stakeholders in marketing, sales, product management and engineering to shape CoreWeave’s growth. You’re eager to listen to customers, understand the mechanics of how we win in the market, and package key insights into scalable value propositions in close collaboration with other teams across the organization.
This is an opportunity to lead the industry with unmatched performance, breakthrough innovations, and differentiated value propositions. It is critical that you leverage data and analytics to demonstrate impact of your work through metrics from adoption to deal flow in a way that is intuitive for your stakeholders and senior executives.
Who You Are:
- Positioning \& Messaging
- Capture impactful positioning and messaging for CoreWeave’s infrastructure services with high value differentiators.
- Develop narratives and demos that show CoreWeave’s differentiation in AI infrastructure services.
- Create compelling launch moments and differentiated, persona\-based messaging based on real customer evidence.
- Provide campaign briefs and input for content marketing and demand generation marketing teams to scale value propositions across the funnel.
- Customer Proof
- Partner with forward\-deployed engineers and lighthouse customers to capture use cases and in\-production benefits.
- Create compelling case studies and customer testimonials that CoreWeave can leverage as customer validations.
- Collaborate with content marketing teams to create unique, differentiated content that carries these customer case studies forward at scale.
- Highlight measurable business outcomes that differentiate CoreWeave’s AI infrastructure services.
- Influence analysts and other critical influencers on how to think about AI infrastructure services in the AI era.
- Establish systematic customer learning capture across high\-value implementations.
- GTM \& Sales Enablement
- Equip Sales and Solution Architects with outcome\-focused deal kits, battlecards, and technical content that resonate with developers and decision\-makers.
- Develop content with strong differentiators that clearly articulates the benefits of CoreWeave’s AI infrastructure.
- Scale messaging through selective partners where their technologies or workloads reinforce CoreWeave’s AI infrastructure story.
- Metrics \& Measurement
- Deliver updated messaging and positioning materials.
- Demonstrate impact of internal GTM tools and assets with sales.
- Map product marketing’s impact on deal velocity and contract value.
- Demonstrate product marketing’s impact on platform adoption and retention.
- Improve competitive win rates in AI infrastructure opportunities.
Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.
- You love turning deeply technical infrastructure concepts into clear, compelling stories — and you've done it for audiences ranging from AI infrastructure engineers to C\-suite buyers.
- You're curious about the mechanics of how AI models actually get trained and deployed at scale — and you instinctively ask "why do customers choose us over the alternative?" before writing a single word of messaging.
- You're an expert at building GTM programs that close the loop between product differentiation and revenue impact — battlecards, case studies, launch moments — and you measure success in pipeline and win rates, not just content volume.
Why CoreWeave?
At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper\-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:
- Be Curious at Your Core
- Act Like an Owner
- Empower Employees
- Deliver Best\-in\-Class Client Experiences
- Achieve More Together
We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!
Basic Qualifications
- 3\+ years in product marketing, technical marketing, product management or GTM–ideally within cloud infrastructure, AI and machine learning, SaaS, or enterprise tech.
- Strong technical foundation with ability to work directly with engineering teams and solution architects.
- Proven track record of building demos, reference content, and narratives that turn technical features into customer adoption.
- Experience with customer journey, use cases, and testimonials
- Proven track record of successful product launches and scaled cross\-functional marketing programs.
- Exceptional communication, influencing, and cross‑functional partnership abilities with both technical and executive audiences.
Preferred but Not Required
- Academic or business background in AI and machine learning
- Experience in GPU/accelerated AI infrastructure ecosystems or AI infrastructure markets.
The base salary range for this role is $157,000 to $210,000\. The starting salary will be determined based on job\-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).
What We Offer
The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.
In addition to a competitive salary, we offer a variety of benefits to support your needs, including:
- Medical, dental, and vision insurance \- 100% paid for by CoreWeave
- Company\-paid Life Insurance
- Voluntary supplemental life insurance
- Short and long\-term disability insurance
- Flexible Spending Account
- Health Savings Account
- Tuition Reimbursement
- Ability to Participate in Employee Stock Purchase Program (ESPP)
- Mental Wellness Benefits through Spring Health
- Family\-Forming support provided by Carrot
- Paid Parental Leave
- Flexible, full\-service childcare support with Kinside
- 401(k) with a generous employer match
- Flexible PTO
- Catered lunch each day in our office and data center locations
- A casual work environment
- A work culture focused on innovative disruption
Our Workplace
While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month. Teams also gather quarterly to support collaboration.
California Consumer Privacy Act \- California applicants only
*CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.*
*As part of this commitment and consistent with the* *Americans with Disabilities Act (ADA), CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact:* *[email protected].*
Export Control Compliance
This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.
Salary Context
This $157K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At CoreWeave, Inc, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $157K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CoreWeave, Inc AI Hiring
CoreWeave, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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