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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.
About the Applied AI Team
The Applied AI team exists to help the world's largest enterprises move beyond AI experimentation and into production. Our mission is to identify, validate, and accelerate high\-impact AI use cases that deliver measurable business outcomes across regulated and data\-intensive industries. Working at the intersection of industry expertise, AI technology, and enterprise data platforms, we partner directly with customers to solve complex business challenges using machine learning, computer vision, predictive analytics, generative AI, and agentic systems, while developing the technical conviction needed to move initiatives from concept to production.
The team combines Applied AI Specialists, strategic ecosystem partners such as NVIDIA, and Forward Deployed Engineers (FDEs) in a unified operating model designed to rapidly move AI initiatives from discovery to deployment. Applied AI Specialists work directly with customers to discover opportunities, validate technical feasibility, and establish technical conviction, while FDE teams build and deploy qualified solutions. Together, we help organizations operationalize AI securely, govern it responsibly, and scale it across the enterprise.
About the Role
As an Applied AI Specialist, your mission is to identify, validate, and establish technical conviction around high\-impact private AI use cases that can be successfully deployed into production. Operating within a specialized regional pod, you will lead technical discovery, execute deep\-dive AI workshops, and collaborate closely with strategic ecosystem partners such as NVIDIA.
You will work directly with enterprise architects, data scientists, platform teams, and business stakeholders to evaluate AI opportunities, assess technical feasibility, and define clear implementation paths. Your focus is on separating low\-conviction ideas from production\-ready opportunities, ensuring that only the highest\-value use cases advance into our engineering pipeline.
What You'll Do
- Lead AI Discovery Workshops \- Partner with strategic ecosystem providers, including NVIDIA, to design and facilitate highly technical hands\-on workshops that identify, validate, and prioritize private AI opportunities.
- Discover and Scope High\-Value Use Cases \- Conduct deep technical and business discovery with enterprise customers to identify AI workloads that can deliver measurable business outcomes and are suitable for production deployment.
- Evaluate Enterprise AI Readiness \- Assess data architecture, governance requirements, infrastructure constraints, security considerations, and data gravity challenges to determine the feasibility of Private AI deployments.
- Develop Regional Domain Leadership \- Serve as a subject matter expert within your regional pod, building deep expertise in one or more strategic industries while helping transfer successful AI patterns across customer segments.
- Build Technical Assets \- Develop lightweight proofs of concept, reference architectures, technical demonstrations, and solution frameworks that help customers validate feasibility before committing to full\-scale implementation.
- Define the Engineering Blueprint \- Translate qualified opportunities into clear technical requirements, data specifications, success criteria, architectural recommendations, and scoped outcomes to ensure seamless handoff to FDE teams.
- Capture and Scale Best Practices \- Document successful use cases, architectural patterns, and implementation frameworks that can be reused across customers, industries, and regions.
What We're Looking For
- The Builder Mindset \- Strong technical foundation in data science. Comfortable working directly with code, APIs, notebooks, development frameworks, and enterprise architectures.
- Hands\-On AI Experience \- Practical experience building or supporting AI applications across machine learning, deep learning, computer vision, time\-series analytics, predictive modeling, anomaly detection, and generative AI. Comfortable discussing model architectures, training and evaluation methodologies, inference patterns, and production deployment considerations across a broad range of enterprise AI workloads.
- Enterprise Discovery and Solutioning Experience \- Proven success in technical pre\-sales, solution architecture, consulting, customer engineering, or similar roles involving the discovery, qualification, and design of complex enterprise technology initiatives.
- Data Readiness Assessment Skills \- Ability to evaluate data quality, governance, security, compliance, operational readiness, and infrastructure constraints that impact successful private AI deployments.
- Executive Communication Skills \- Exceptional ability to engage technical and executive audiences, facilitating conversations that connect business outcomes with practical implementation strategies.
- Technical Proficiency \- Experience with Python, APIs, cloud\-native architectures, data pipelines, MLOps, and modern AI development ecosystems.
Preferred Qualifications
- Experience working with NVIDIA AI Enterprise, NIMs, NeMo, RAPIDs or accelerated computing platforms.
- Experience delivering customer\-facing workshops, design sessions, or architecture engagements.
- Experience moving AI projects from proof of concept into production environments.
- Familiarity with enterprise data platforms, governance frameworks, and hybrid cloud architectures.
This role is not eligible for immigration sponsorship.
The anticipated annual base salary range for this position is:
Washington: $171,000 \- $200,000
What We Offer
Centralized Platform Support \- You will never lose time troubleshooting cloud credentials, environment setup, or workshop preparation. A dedicated Global Workshop Platform Engineer builds, seeds, manages, and tears down workshop environments programmatically, allowing you to focus entirely on customer engagement and solution discovery.
Clean Operational Boundaries \- Your mission is discovery, validation, and qualification. Once a use case has achieved technical conviction and is approved for execution, a dedicated FDE Pod assumes ownership of implementation and production delivery. This allows you to remain focused on uncovering the next strategic opportunity.
Direct Access to Industry Innovation \- You will work alongside leading AI practitioners, strategic ecosystem partners, and enterprise innovators to help shape how AI is deployed across some of the world's largest organizations.
High\-Impact Culture \- You will be part of a highly visible, practitioner\-led organization with executive sponsorship and a clear mandate to drive measurable business outcomes through AI.
Career Growth \- As the Applied AI organization expands globally, you will have opportunities to deepen industry expertise, influence go\-to\-market strategy, mentor future practitioners, and help define the operating model for one of the company's most strategic growth initiatives.
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\-MH2
\#LI\-REMOTE
Salary Context
This $171K-$200K 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 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 ($185K) sits 14% below the category median. Disclosed range: $171K to $200K.
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
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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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