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About This Role
Job Description:
The Director, Analytics Engineering and AI leads the data engineering group and owns the curated, certified data foundation the business relies on to make decisions. The leader is accountable for a cohesive, coordinated data model covering both product and enterprise data. They also build the certified semantic layer that makes this foundation reliably and safely accessible to AI. They lead a team of senior and principal engineers through a significant platform transition towards AI while sustaining the revenue\-, pipeline\-, and finance\-critical reporting the company depends on every day. This is a hands\-on, player\-coach role: the right leader sets direction and grows the team, but also rolls up their sleeves — modeling data, writing and reviewing SQL and dbt code leveraging AI, and getting into the weeds to solve the hardest problems alongside their engineers.
Who you’re committed to being:
- You move fluidly between technical detail and executive\-level storytelling, adjusting your message to fit the room.
- You build trust and alignment among Revenue, Marketing, and Finance by earning agreement, not demanding it.
- You balance vision with execution.
- Training and elevating other analytics practitioners is part of how you define your own success.
- You treat your solutions and leadership style as always improving, not finished.
What you’ll do:
- Lead, develop, and grow the Analytics Engineering team — setting technical direction, standards, and priorities — while sustaining business\-critical reporting (revenue, product, marketing, finance) without interruption.
- Own a single, unified data transformation and model spanning both product/behavioral and enterprise (GTM, finance) data.
- Establish and expand the certified contextual layer that ensures reliable and safe access to the data warehouse for analytics. This layer builds on a governed data dictionary and metrics store. It also covers lineage, freshness, ownership, access, entity relationships, and business knowledge.
- Drive the migration of transformation logic out of integration/middleware tooling into governed, certified models owned by the team.
- Partner across Data Analytics, Data Engineering, Data Architecture, Data Governance to plan and execute cross\-team initiatives.
- Serve as a senior partner to collaborators across Revenue, Marketing, Finance, GTM, and Success. Translate business needs into scalable, balanced analytics solutions. Communicate mentorship, compromises, and outcomes to senior leadership.
- Set and uphold engineering standards — modeling conventions, certification practices, code quality, and documentation.
- Stay hands\-on in the work: build and review data models, write and debug SQL and dbt code leveraging AI, dig into data\-quality issues, and take direct ownership of the most complex or highest\-stakes problems.
Experience you’ll bring:
- Familiarity with high\-performance OLAP serving layers (e.g., ClickHouse) for product\- or customer\-facing analytics.
- Experience reducing reliance on integration/middleware tooling by migrating transformation logic into governed models.
- Experience partnering with a data governance function on company\-wide standards, schema/version control, and PII classification.
Requirements:
- Requires a minimum of 12 years of related or equivalent experience; or 8\+ years and an advanced degree.
- Demonstrated hands\-on expertise in data curation, transformation, and dimensional/medallion data modeling on a modern cloud data stack (e.g., Snowflake, dbt).
- Proven breadth of data modeling across both product/behavioral event data and enterprise GTM/finance data.
- Current, hands\-on technical depth in making productive use of AI. This is a player\-coach role, not a purely managerial one.
- Experience leading a team of senior and principal engineers through a significant platform or architecture transition — not only steady\-state delivery, but change.
- Strong grasp of data warehousing architecture, data governance.
- Ability to clearly communicate direction, trade\-offs, and results to senior, non\-technical leaders as well as technical team members.
- Experience building a semantic/contextual layer and making data AI\-ready — including patterns such as retrieval\-augmented generation (RAG) or natural\-language analytics over a governed warehouse.
- This is a remote role; however, applicants located within 45 miles of our Westlake/Dallas, TX office should expect to work on\-site Tuesday through Thursday, with remote flexibility on Mondays and Fridays. This approach enables more effective collaboration, quicker decision\-making, and a stronger culture, while still providing flexibility.
Why you’ll love working here:
- We work in a blended environment that supports collaboration, flexibility, and connection across teams.
- We are mission\-driven, shaping the future of tech upskillling and delivering impact that matters.
- We foster a culture of inclusion and belonging, where everyone can contribute and thrive.
- We are always learning, creating an environment where you can take on new challenges, expand your skills, and grow with purpose.
- Benefits include competitive compensation, bonus eligibility, comprehensive medical coverage, unlimited PTO, wellness reimbursement, professional development funds, and more.
About us:
Pluralsight provides the only learning platform dedicated to accelerating the technology skills and capabilities of today’s tech workforce. Thousands of companies, government organizations and individuals around the world rely on Pluralsight to support critical technology skill development in areas that are crucial to innovation including artificial intelligence, cloud computing, cybersecurity, software development, and machine learning. We offer highly curated content developed by vetted technology experts, industry leading skill assessments, and hands on, immersive learning experiences designed to help individuals skill\-up faster.
Physical Requirements:
This role is primarily performed in an office or home office setting and involves standard computer\-based work.
EEOC \& Accommodations Statement:
Bring yourself. Pluralsight is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or veteran status. We also consider qualified applicants with criminal histories, consistent with EEOC guidelines and local laws.
If you need an accommodation to apply, interview, or perform essential job functions,
Pay Transparency:
The annual US base salary range for this role is $167,200\- $220,000 USD. Actual compensation will depend on location, skills, experience, and other factors. Additional benefits and bonuses may apply.
Applications must be submitted within 90 days after the initial posting date to be considered.
Recruiting Scam Notice:
Please be aware of recruiting scams. We’ll only contact you from an @pluralsight.com email or verified channels. We never ask for sensitive personal info or payments as part of the hiring process. All openings are posted on our Careers page.
\#LI\-SD1
### About Us
Pluralsight provides the only learning platform dedicated to accelerating the technology skills and capabilities of today’s tech workforce. Thousands of companies, government organizations, and individuals around the world rely on our platform to support critical technology skill development in areas that are crucial to innovation, including artificial intelligence, cloud computing, cybersecurity, software development, and machine learning.
We offer highly curated content developed by vetted technology experts, industry leading skill assessments, and hands\-on, immersive learning experiences designed to help individuals skill\-up faster.
Salary Context
This $167K-$220K range is above the median 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 Pluralsight, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($193K) sits 11% below the category median. Disclosed range: $167K to $220K.
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.
Pluralsight AI Hiring
Pluralsight has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $145K - $220K.
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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