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About This Role
About The Team
Rubrik is on a mission to secure the world’s data and our Legal Team is committed to supporting this mission, offering critical guidance across many areas of the business.
About The Role
Our growing AI Legal Team is looking for a lawyer who has a deep interest and understanding of artificial intelligence (AI) and is excited by the prospect of exploring the expanding legal landscape raised by this cutting edge technology. Ongoing advances in AI bring the promise of substantial gains in innovation and productivity but also raise complex issues relating to intellectual property, confidentiality, privacy, security, and more.
As a Sr. AI Counsel, you will help guide Rubrik’s continuing journey to harness the power of AI while mitigating risks. In this role, you will drive continuous improvements in our approach to AI and strengthen our review processes and controls, empowering Rubrik to embrace the power and promise of AI in a safe, responsible, and transparent manner. We are looking for a lawyer who is technologically inclined, is a pragmatic and business\-oriented problem solver, likes to move quickly, and enjoys cross\-functional collaboration, as you will be partnering with many teams, including Product, Engineering, Information Security, and of course, various teams across Legal.
What You’ll Do
- Ensure that our use of AI complies with laws and regulations, meets our contractual obligations, and aligns with our security requirements
- Enable adoption of AI tools to boost productivity and the launch of AI\-powered products and features while mitigating legal, compliance, security, and business risks
- Draft and negotiate AI terms in contracts with our vendors and our customers
- Coordinate our review processes for use of generative AI and other advanced AI technologies
- Educate our employees on AI issues
Improve our AI review processes to make them more effective, comprehensive, and efficient
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Experience You’ll Need
- Licensed attorney and law degree from an accredited law school
- 5\+ years of relevant legal experience, including in\-house experience at a technology company
- Familiarity with AI models, concepts, and tools (e.g., machine learning, LLMs, NLP, retrieval augmented generation, AI agents and agentic AI)
- Familiarity with AI\-related regulations and frameworks, including the EU AI Act, NIST AI RMF
- Experience in one or more of the following areas:
- + Data and privacy regulations
+ Customer contracts/licensing
+ Vendor contracts/procurement
+ Copyright / technology law
+ Product review and counseling
+ Industry compliance standards
- Comfortable diving into technical details and able to quickly learn new technologies
- Software\-related technical expertise, experience, or educational background
- Excellent communication and organizational skills
- Ability to prioritize and manage simultaneous projects in a fast\-paced environment
The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job\-related skills, experience, and relevant education or training.
US (SF Bay Area, DC Metro, NYC, Seattle) Pay Range
$177,800—$266,600 USD
The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits. The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location. Within the range, the salary offered will be determined by work location and additional factors, including job\-related skills, experience, and relevant education or training.
US2 (all other US offices/remote) Pay Range
$160,000—$240,000 USD
Join Us in Securing and Accelerating the World's AI Transformation
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Rubrik (RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real\-time guardrails, fine\-tuning for accuracy and undoing agentic mistakes.
Inclusion @ Rubrik
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At Rubrik, we are dedicated to fostering a culture where people from all backgrounds are valued, feel they belong, and believe they can succeed. Our commitment to inclusion is at the heart of our mission to secure the world’s data.
Our goal is to hire and promote the best talent, regardless of background. We continually review our hiring practices to ensure fairness and strive to create an environment where every employee has equal access to opportunities for growth and excellence. We believe in empowering everyone to bring their authentic selves to work and achieve their fullest potential.
### Our inclusion strategy focuses on three core areas of our business and culture:
- Our Company: We are committed to building a merit\-based organization that offers equal access to growth and success for all employees globally. Your potential is limitless here.
- Our Culture: We strive to create an inclusive atmosphere where individuals from all backgrounds feel a strong sense of belonging, can thrive, and do their best work. Your contributions help us innovate and break boundaries.
- Our Communities: We are dedicated to expanding our engagement with the communities we operate in, creating opportunities for underrepresented talent and driving greater innovation for our clients. Your impact extends beyond Rubrik, contributing to safer and stronger communities.
Equal Opportunity Employer/Veterans/Disabled
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Rubrik is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Rubrik provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Rubrik complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please contact us at hr@rubrik.com if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.
Salary Context
This $177K-$266K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rubrik, 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. Disclosed range: $177K to $266K.
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.
Rubrik AI Hiring
Rubrik has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $266K - $266K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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