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About This Role
Business Area:
Corp. StrategySeniority Level:
Mid\-Senior levelJob Description:
At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
Job title: Manager, Applied AI Strategy and Operations
Job Purpose:
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As a Manager, Applied AI Strategy and Operations within the Chief Business Officer and GM, Applied AI’s organization, you will lead high\-impact, global commercial and operational initiatives with a direct emphasis on accelerating Cloudera's Private AI growth.
Our Applied AI organization is a fast\-moving, dedicated group of Forward Deployed Engineers (FDEs), Industry \& Applied AI Specialists, and business strategy leaders committed to establishing Cloudera as the trusted standard for Private AI in the enterprise. In this high\-autonomy, high\-trust role, you will act as the strategic and analytical backbone behind our field engineering motions. You will prioritize the right initiatives across our strategic accounts, establish robust governance and operating rhythms, conduct deep\-dive portfolio and sales pipeline analyses, and synthesize complex technical and commercial signals into actionable recommendations for Cloudera's senior leadership.
Key responsibilities:
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- Drive Global Governance \& Operating Rhythms: Establish, evolve, and manage the programmatic operating rhythm for the global Applied AI organization across AMER, EMEA, and APAC. Lead the monthly business review (MBR) process—from metric gathering and CRM ingestion through deep quantitative analysis and executive synthesis for senior leadership.
- Manage the AI Adoption Funnel: Partner with regional FDE and Applied AI Specialist leadership to track, analyze, and optimize customer progression through our four core execution stages: Discovery \& Strategy, Solution \& Value, Pilot \& Prove, and Production \& Scale.
- Account Prioritization \& Use Case Frameworks: Structure analytical frameworks grounded in real customer signal—joining technical discovery sessions, reviewing triage outcomes, and working directly with field engineering teams to filter and isolate high\-ROI Private AI workloads. Conduct rigorous analyses to down\-select and prioritize target accounts across our Top 100 strategic enterprise lists.
- Define \& Standardize Strategic Metrics: Collaborate with central Revenue Operations and AI GTM Sales Strategy to define, refine, and standardize key performance indicators around subscription and consumption\-based Private AI adoption. Track organizational progress against critical milestones, including customer pitch meetings, on\-site Use Case Workshops, Applied AI Hands\-on Labs, and deployed production solutions.
- Ecosystem \& Commercial Strategy: Analyze and support go\-to\-market operational alignment with major AI infrastructure and ecosystem partners (including NVIDIA Blueprints and VAST). Evaluate revenue share models, joint sales plays, and co\-deployment strategies to maximize platform pull\-through and annual recurring revenue (ARR) expansion.
- Cross\-Functional Leadership \& Solution Productization: Act as an embedded, highly trusted thought partner to technical and commercial leaders. Design and implement scalable processes for tracking team allocations and solution productization, ensuring repeatable delivery patterns are codified and communicated across Account Executives, Solution Engineers, and Professional Services globally.
- Business Planning \& Executive Storytelling: Build comprehensive segment analyses, growth models, and strategic business cases to support critical investment decisions, headcount planning, and resource allocation across regional pods.
Preferred Qualifications:
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- Strong problem\-solving and structuring skills, with a proven ability to scope complex, ambiguous strategic challenges and identify root causes within technical go\-to\-market organizations.
- Highly analytical and excellent at leveraging facts, CRM data, and customer insights to generate and validate strategic hypotheses regarding enterprise AI adoption and workload consumption.
- Proven ability to communicate and build trust with stakeholders at all levels—including C\-suite executives, technical engineering leaders, and regional sales VPs—by turning granular data and research into actionable insight and a well\-structured executive storyline.
- Deep familiarity with enterprise data management platforms, hybrid cloud architectures, data lakehouses (e.g., Apache Iceberg), and the broader AI/ML ecosystem capabilities.
- Strong quantitative, financial modeling, and data manipulation skills; proficiency in GTM systems (e.g., Salesforce, Clari), business intelligence tools (e.g., Tableau, Power BI), and SQL is a strong plus.
- Demonstrated capacity to operate with high autonomy in dynamic, fast\-moving environments, building structure and operational rigor from the ground up rather than inheriting legacy processes.
Preferred Experience:
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- 6\+ years of progressive work experience in sales strategy, revenue operations, business operations, or management consulting, with a demonstrated track record of driving cross\-functional alignment and measurable business impact.
- Direct experience supporting specialized technical sales, pre\-sales, or post\-sales delivery teams—particularly within Forward Deployed Engineering (FDE), Solution Architecture, or Applied AI Specialist organizations.
- Deep domain experience in enterprise software, big data analytics, machine learning/AI, or hybrid cloud infrastructure companies.
- Proven expertise working with cloud subscription, consumption\-based (usage\-driven), or product\-led growth (PLG) business models, specifically tracking workload transition from pilot to production scale.
- Experience supporting enterprise commercial or technical organizations through rapid 2–3x\+ revenue growth periods and global expansion across AMER, EMEA, and APAC regions.
- *Note: Candidates without direct commercial ownership experience will still be strongly considered if they demonstrate long, deep tenure supporting commercial or technical sales leaders in a closely comparable enterprise GTM model.*
- The right person in this role has an opportunity to make a huge impact at Cloudera and add value to our future decisions. If this position has piqued your interest and you have what we described \- we invite you apply!
- This role is not eligible for immigration sponsorship.
- *The anticipated annual base salary range for this position is:*
- *Washington: $144,000 \- $180,000*
- *New York: $144,000 \- $180,000*
- *California: $144,000 \- $180,000*
Individual compensation within the published range is determined by the candidate's skills, experience, qualifications, and primary work location. In addition to base pay, sales roles are eligible for Cloudera's commission plan, while non\-sales roles are eligible for the corporate incentive plan. All employees receive a comprehensive benefits package
What you can expect from us:
- Generous PTO Policy
- Support work life balance with Unplugged Days
- Flexible WFH Policy
- Mental \& Physical Wellness programs
- Phone and Internet Reimbursement program
- Access to Continued Career Development
- Comprehensive Benefits and Competitive Packages
- Paid Volunteer Time
- Employee Resource Groups
EEO/VEVRAA
LI\-SF1
Salary Context
This $144K-$180K range is below 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 Cloudera, 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. This role's midpoint ($162K) sits 25% below the category median. Disclosed range: $144K to $180K.
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.
Cloudera AI Hiring
Cloudera has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, Washington, DC, US, New York, NY, US. Compensation range: $180K - $247K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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.
Frequently Asked Questions
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