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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
Despite the "Engineer" title, this isn't a typical back\-end coding job. This is a pre\-sales and architectural advisory role for someone who is part scientist, part consultant, and part communicator. At the Staff / Principal level, you are expected to be a hybrid heavy hitter — someone who can talk shop with PhD data scientists and then walk into a boardroom and explain the ROI to a CEO.
We aren't looking for your average expert. We're building a team of T\-shaped professionals with:
- Deep Technical Roots — You know PyTorch, Kubernetes, and GPU clusters (NVIDIA/AMD) inside and out, and you know when XGBoost is a better choice than the latest frontier model.
- Broad Business Acumen — You understand verticals, e.g. how a hospital's AI needs differ from a hedge fund's, and can navigate both conversations with credibility.
- Collaborative Autonomy — With a global remit and potential for 30\-60% travel, you are in charge of driving results within diverse collaborations.
We're building a small, global Applied AI team to work directly with enterprise customers and partners — helping them turn AI ambitions into production outcomes on Pure's platform. This team brings genuine research and production depth to customer\-facing engagements: leading technical advisory sessions, shaping AI strategy for some of the world's largest companies, and contributing to Pure's growing reputation in AI through publications, conferences, and thought leadership.
The team also ensures Pure's account teams can effectively qualify and position AI advisory opportunities across their territories, and provides enablement to our internal field and channel partner communities. The role requires working cross\-functionally across Solutions, Marketing, Alliances, Engineering, Enablement, and Sales to align initiatives and go\-to\-market campaigns — all while maintaining genuine technical depth in enterprise AI.
If this sounds like you, come join one of the most exciting teams at Pure.
WHAT YOU'LL DO:
- Lead technical discovery and advisory engagements with customers to identify high\-value AI use cases relevant to their industry and data
- Advise on end\-to\-end AI deployment — model selection, training/fine\-tuning strategies, inference optimization, data pipeline design, and evaluation/alignment/safety frameworks — optimized for Pure's platform
- Collaborate cross\-functionally to qualify and position AI opportunities, and develop proof\-of\-value prototypes that translate technical performance into business outcomes
- Create AI enablement content for field teams, partners, and customers — technical walkthroughs, qualification guides, workshops, and vertical\-specific use case frameworks
- Present at industry conferences, publish technical content and peer\-reviewed research, and represent Pure in engagements with strategic technology partners (NVIDIA, AMD, cloud providers, MSPs)
- Operate autonomously in ambiguous environments — independently scoping high\-impact initiatives and driving them to completion at the right pace
WHAT YOU BRING:
- Advanced degree in a quantitative field (Computer Science, Physics, Mathematics, Engineering, or related), or equivalent demonstrated through publications and production system experience
- 8\+ years building and deploying AI/ML systems in cloud or on\-prem environments
- Deep expertise in modern AI/ML — large language models, distributed training, inference optimization, agentic systems, evaluation/alignment frameworks, and classical ML
- Fluency with the modern AI stack (PyTorch, vLLM, Ray, Kubernetes) and data platforms (Spark, Snowflake, Kafka, etc.)
- Able to scope and carry out research projects that support critical business objectives, both independently and collaboratively
- Excellent written, verbal, and presentation skills — equally clear with hands\-on data scientists and C\-suite decision\-makers
WHAT SETS YOU APART:
- Experience with large\-scale AI infrastructure: GPU clusters, high\-performance storage, containerized deployments
- Experience in customer\-facing technical advisory or consulting roles with enterprise accounts
- Experience leading independent, multi\-year research from concept to publication or large\-scale production deployment
- Exposure to multiple industry verticals (financial services, healthcare, telco, manufacturing)
- Publication record, conference presentations, or recognized technical presence
- Background combining applied research with shipping production systems
- Familiarity with CRM/opportunity management systems (Salesforce preferred)
\#LI\-REMOTE
\#LI\-JL4
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\[email protected] 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 $171K-$257K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $171K to $257K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Everpure AI Hiring
Everpure has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $257K - $283K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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