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About This Role
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About Commvault
Commvault (NASDAQ: CVLT) is the gold standard in cyber resilience. The company empowers customers to uncover, take action, and rapidly recover from cyberattacks – keeping data safe and businesses resilient. The company’s unique AI\-powered platform combines best\-in\-class data protection, exceptional data security, advanced data intelligence, and lightning\-fast recovery across any workload or cloud at the lowest TCO. For over 25 years, more than 100,000 organizations and a vast partner ecosystem have relied on Commvault to reduce risks, improve governance, and do more with data.
Commvault Marketing is in the middle of a real AI transformation, and we are looking for someone to help build it!
This is an early\-career role for someone with a marketing background and a genuine curiosity about AI. You will be hands\-on from day one, building AI\-powered agents \& workflows for marketing teams across the org, supporting an enablement program that helps people actually adopt the tools, and helping keep our AI governance process running smoothly day to day.
You will work closely with senior leaders who own the AI Marketing strategy and will invest in your development. The work is new, the pace is fast, and the learning curve is real. If that sounds exciting rather than intimidating, this role is for you.
#### Responsibilities:
- Build and iterate on agents \& AI\-powered workflows to help marketing teams work more efficiently
- Support a recurring AI enablement program including office hours, adoption coaching, and team training sessions
- Help triage and route incoming AI use case requests to keep the program pipeline moving
- Collaborate with other teams to stay aligned on priorities and progress
- Document workflows and build a library of reusable prompts so the team can scale what works
- Support the onboarding of new AI tools and help the team get up to speed as the tech evolves
*As the program grows, so will this role. Additional opportunities and responsibilities will emerge over time.*
#### Requirements:
- Marketing background through a degree, internship, or early work experience in a marketing or marketing operations role
- Strong written communication and sharp attention to detail
- Quick learner who is comfortable picking up new tools and figuring things out independently
- Positive, can\-do attitude and genuine curiosity about AI and its potential in marketing
- Comfortable in a fast\-paced environment where priorities shift and not everything comes with a playbook
#### Highly desirable:
- Hands\-on experience building AI agents or automated workflows. Given how new this space is, we do not expect this to be common. If you have experimented on your own, tell us.
- Familiarity with AI assistants, large language models, or automated workflow tools
- Exposure to prompt engineering or AI workflow design
- Experience supporting training or enablement programs
#### You’ll love working here because:
- Continuous professional development, product training, and career pathing
- An inclusive company culture and the opportunity to join our Employee Resource Groups (ERGs)
- Generous benefits supporting your health, financial security, and work\-life balance
- Employee stock purchase plan (ESPP)
\#LI\-AM1
\#LI\-Remote
Thank you for your interest in Commvault. Reflected below is the minimum and maximum base salary range for this role. At Commvault we use broad salary ranges in our job postings to reflect the diverse levels of expertise and experience among our candidates and is not reflective of the total compensation and benefits package. The specific salary offered will be determined based on your unique qualifications, including your relevant experience, skills, and the value you bring to the role. While the range provides a general idea of the compensation, it is important to note that placements within the range are not automatic and will be carefully considered to ensure a fair and competitive offer. We are committed to rewarding talent and experience.
Pay Range
$54,400—$100,050 USD
Commvault is an equal opportunity workplace and is an affirmative action employer. We are always committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status and we will not discriminate against on the basis of such characteristics or any other status protected by the laws or regulations in the locations where we work.
Commvault’s goal is to make interviewing inclusive and accessible to all candidates and employees. If you have a disability or special need that requires accommodation to participate in the interview process or apply for a position at Commvault, please email accommodations@commvault.com For any inquiries not related to an accommodation please reach out to wwrecruitingteam@commvault.com.
Salary Context
This $54K-$100K range is in the lower quartile 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 Commvault, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($77K) sits 65% below the category median. Disclosed range: $54K to $100K.
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.
Commvault AI Hiring
Commvault has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $100K.
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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