Interested in this AI/ML Engineer role at CoreWeave?
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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:
The Strategy and Transformation team builds intelligent internal systems that improve how CoreWeave operates. The team partners with groups including Finance, Supply Chain, HR, and core business operations to develop AI-powered workflows and first-party applications that connect enterprise platforms such as ERP, HRIS, MRP, PDM, and DCIM.
We're looking for a curious, energetic, and innovative Software and AI Engineer to join our Strategy and Transformation team. You'll help build intelligent AI agents and full-stack applications supporting Finance, Supply Chain, HR, Legal and Data center operations, integrating with systems such as ERP, HRIS, MRP, PDM, and DCIM.
This role is ideal for an early-career engineer with solid programming fundamentals who wants to grow at the intersection of software engineering, product development, and applied AI.
About the role:
As a Software and AI Engineer, IT Systems, you'll develop well-defined features, application components, and AI workflows with guidance from experienced engineers. You'll contribute across the development lifecycle, including implementation, testing, deployment, documentation, and production support.
You'll gain hands-on experience with AI frameworks and APIs such as LangChain, LangGraph, OpenAI, Gemini, and Glean; integration platforms such as Workato or Boomi; React-based interfaces; and backend services written in Node.js, Go, or Python.
Who You Are:
- Develop and maintain scoped features for internal AI agents, copilots, and first-party applications.
- Build user interfaces using React, TypeScript, and modern frontend frameworks.
- Implement backend services and automation scripts for data processing and workflow orchestration.
- Integrate LLM APIs and AI capabilities into applications using established designs and engineering patterns.
- Build and maintain APIs and enterprise integrations under the guidance of experienced engineers.
- Write tests, troubleshoot defects, and participate in code reviews to improve application quality.
- Add logging, monitoring, and error handling to support reliable application operation.
- Follow established security, access control, deployment, and documentation practices.
- Use AI development tools such as Cursor, Claude, GitHub Copilot, and Codex, reviewing and validating generated code.
- Collaborate with engineers and business stakeholders to understand requirements and incorporate feedback.
- Take ownership of assigned work, communicate progress, and raise blockers promptly.
Key Qualifications:
- 2+ year of hands-on software development experience, including relevant internships, co-op assignments, or substantial project work.
- Working proficiency in one or more programming languages such as TypeScript, Python, Go, Java, C#, or C++.
- Solid understanding of programming fundamentals, data structures, debugging, and software testing.
- Good understanding of system design concepts and the ability to evaluate tradeoffs across performance, reliability, cost, and maintainability, with guidance from experienced engineers.
- Experience building web applications or backend services using frameworks such as React, Next.js, Node.js, or FastAPI.
- Familiarity with RESTful APIs, databases, and basic application integration concepts.
- Familiarity with Git, collaborative development, and CI/CD concepts.
- Basic understanding of cloud platforms such as AWS, Azure, or GCP.
- Exposure to LLM APIs, AI frameworks, or AI-powered application development through professional, academic, or personal projects.
- Ability to learn new technologies, incorporate feedback, and complete well-defined tasks with guidance.
- Clear communication skills and an interest in building tools that improve business productivity and employee experience.
Preferred:
- Experience building an AI-powered application, chatbot, or automation workflow.
- Exposure to LangChain, LangGraph, OpenAI, Gemini, Glean, or similar frameworks and APIs.
- Familiarity with Workato, Boomi, or other integration and automation platforms.
- Experience scripting in Python or Bash.
- Exposure to containers, application monitoring, or cloud deployment.
- Interest in Finance, Supply Chain, HR, or other enterprise business workflows.
- Attention to user experience, code quality, and maintainability.
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 enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities 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!
The base salary range for this role is $120,000 to $160,000. The starting salary will be determined by job-related knowledge, skills, experience, and the 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. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include:
- 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
California Applicants
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Equal Opportunity & Accommodations
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 $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CoreWeave, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CoreWeave AI Hiring
CoreWeave has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span Dallas, TX, US, Bellevue, WA, London, England, UK. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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