Interested in this AI/ML Engineer role at Everpure?
Apply Now →About This Role
We're in an unbelievably exciting area of tech and are fundamentally reshaping the data storage industry. Here, you lead with innovative thinking, grow along with us, and join the smartest team in the industry.
This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.
### THE ROLE
Everpure's Data and Analytics (DnA) organization is seeking a Senior Manager, Finance AI Transformation to help Finance run AI, automation, analytics, and data as a core operating strategy rather than a side project. This role combines deep finance expertise with hands\-on AI and transformation leadership to drive meaningful change across the Office of the CFO.
You will serve as the business\-facing strategy and adoption lead for DnA's AI Factory for Finance, helping identify high\-value opportunities, build and validate solutions, and scale successful capabilities across Finance. Working closely with leaders across Accounting, Revenue, Order Management, and GTM Finance, you will help translate trusted data and AI capabilities into measurable improvements in productivity, controls, and executive decision\-making.
This is a highly collaborative role that partners across Finance, DnA, and IT to bring AI\-enabled solutions to life while establishing the governance, controls, and adoption frameworks necessary for long\-term success.
### WHAT YOU'LL DO
- Partner with Finance teams to identify high\-value AI and automation opportunities and redesign underlying processes to enable AI\-driven workflows.
- Prioritize opportunities, validate concepts through focused pilots, and scale successful solutions with measurable business outcomes and adoption plans.
- Act as the bridge between Finance, DnA, and IT/Business Systems by translating business requirements into process, data, system, and control requirements.
- Help design trusted finance data products, semantic layers, workflows, and AI\-enabled capabilities that reduce manual effort and improve decision\-making.
- Build the operating cadence for transformation delivery, including intake processes, business cases, prioritization, milestones, risk management, stakeholder communications, adoption metrics, and benefits realization.
- Drive adoption of Finance AI capabilities through user acceptance testing, training, launch communications, operating procedures, and ongoing feedback loops.
- Define and track value realization metrics including cycle\-time reduction, productivity improvements, adoption rates, control enhancements, quality gains, and stakeholder satisfaction.
- Partner with Finance leadership to ensure AI and automation initiatives align with business priorities and operational objectives.
We are primarily an in\-office environment and therefore, you will be expected to work from the Santa Clara, CA office in compliance with Everpure's policies, unless you are on PTO, work travel, or other approved leave.
### WHAT YOU BRING
- 8\+ years of experience in finance transformation, accounting, revenue or GTM finance, finance operations, consulting, business systems, data and analytics, or related functions.
- Strong understanding of core finance and accounting processes, including close, general ledger, revenue recognition, Quote\-to\-Cash, Order\-to\-Cash, Record\-to\-Report, controls, and related finance operations.
- Demonstrated experience evaluating, piloting, or deploying AI\-enabled capabilities such as Generative AI, agentic AI workflows, machine learning, predictive analytics, intelligent automation, or workflow solutions within Finance or related functions.
- Hands\-on experience experimenting with emerging AI technologies and building practical prototypes using modern AI tools.
- Experience with finance systems and platforms including ERP, CRM, CPQ, billing, close and reconciliation tools, cloud data platforms, and business intelligence solutions.
- Ability to define requirements for finance data products, semantic layers, dashboards, and AI\-enabled capabilities.
- Strong understanding of responsible AI principles, data governance, access controls, auditability, model risk, privacy, SOX considerations, and human\-in\-the\-loop review processes.
- Proven ability to lead cross\-functional transformation initiatives through influence, aligning business leaders, technical teams, and control partners around measurable outcomes.
- Excellent communication skills with the ability to translate complex finance, data, and technology concepts into clear executive updates, business cases, and adoption plans.
- Ability to manage multiple complex initiatives simultaneously while operating effectively in a fast\-paced, evolving environment.
\#LI\-TH3, \#LI\-ONSITE
WHAT YOU CAN EXPECT FROM US:
- Innovation: We celebrate those who think critically, like a challenge, and aspire to be trailblazers.
- Growth: We give you the space and support to grow along with us and to contribute to something meaningful. We have been named Fortune's Best Workplaces in Technology™, Fortune's Best Workplaces in the Bay Area™, and certified as a Great Place to Work®!
- Team: We build each other up and set aside ego for the greater good.
And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources, and company\-sponsored team events. Check out purebenefits.com for more information.
ACCOMMODATIONS AND ACCESSIBILITY:
Candidates with disabilities may request accommodations for all aspects of our hiring process. For more on this, contact us at TA\-Ops@purestorage.com if you're invited to an interview.
OUR COMMITMENT TO A STRONG AND INCLUSIVE TEAM:
We're forging a future where everyone finds their rightful place and where every voice matters. Where uniqueness isn't just accepted but embraced. That's why we are committed to fostering the growth and development of every person, cultivating a sense of community through our Employee Resource Groups and advocating for inclusive leadership.
Everpure is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other characteristic legally protected by the laws of the jurisdiction in which you are being considered for hire.
Join us and bring your best.
Bring your bold.
Pure and simple.
Salary Context
This $192K-$307K 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 Everpure, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($249K) sits 14% above the category median. Disclosed range: $192K to $307K.
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.
Everpure AI Hiring
Everpure has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $307K - $307K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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