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About This Role
At SolarWinds, we're a people\-first company. Our purpose is to enrich the lives of the people we serve—including our employees, customers, shareholders, partners, and communities. Join us in our mission to help customers accelerate business transformation with simple, powerful, and secure solutions.
The ideal candidate thrives in an innovative, fast\-paced environment and is collaborative, accountable, ready, and empathetic. We're looking for individuals who believe they can accomplish more as a team and create lasting growth for themselves and others. We hire based on attitude, competency, and commitment. Solarians are ready to advance our world\-class solutions in a fast\-paced environment and accept the challenge to lead with purpose. If you're looking to build your career with an exceptional team, you've come to the right place. Join SolarWinds and grow with us!
About the Role
SolarWinds is hiring a Field Enablement Manager, AI Solutions to join the Revenue Enablement team. Reporting to the Director of Revenue Enablement and based in Austin, TX, this mid\-level individual contributor role is responsible for enabling the field on the advanced SolarWinds AI capabilities—both current platform capabilities and future AI launches across the product roadmap.
This person will serve as the subject matter expert for the Revenue Enablement team on SolarWinds AI, partnering closely with Product, Solutions Engineering, and PMM to translate SolarWinds AI capabilities into field\-ready programs, content, and coaching. You will work alongside teammates who own global onboarding, field enablement, instructional design, reporting, and GTM communications. Your focus is ensuring sellers can speak fluently about AI in general and confidently position and sell SolarWinds AI.
What You'll Do
- AI Product Knowledge \& Launch Readiness
+ Serve as the Revenue Enablement SME on SolarWinds AI capabilities across the entire portfolio and upcoming roadmap plans.
+ Partner with Product, Solutions Engineering, PMM, and other GTM teams to translate AI feature launches into field\-ready training, talk tracks, and learning programs ahead of go\-to\-market.
+ Build and maintain launch readiness programs for new AI capabilities, ensuring the field is prepared and confident at launch.
+ Help the field build and maintain strong understanding across the AI landscape to ensure they are well positioned to speak with confidence to buyers about their AI journey.
- Coaching \& Facilitation
+ Coach reps and managers on how to position SolarWinds AI to technical and business buyers by assessing knowledge, identifying gaps, and running targeted training.
+ Deliver live and virtual AI\-focused training and communications to GTM customer\-facing teams.
- Content Development
+ Co\-build and maintain a content library of AI\-focused pitch decks, talk tracks, battlecards, demo guides, and objection handling for SW1 AI use cases in partnership with PMM.
+ Partner with instructional designers, content creators, and marketing to develop structured learning experiences around SolarWinds AI capabilities.
+ Manage AI enablement assets in the LMS and CMS (Letter.AI and SharePoint).
- Cross\-Functional Partnership
+ Build trusted relationships with Product and Marketing leadership to gain early visibility into AI roadmap decisions, ensuring Revenue Enablement can shape field requirements before launch rather than react to them.
+ Collaborate with the GTM communications manager on AI\-focused enablement content, competitive positioning, and field communications.
+ Work with the reporting function to track enablement program effectiveness and surface insights that inform program decisions.
- AI\-Enabled Operations
+ Use AI tools such as Glean, Claude Cowork, and other internal platforms to improve speed, quality, and efficiency across daily work.
+ Identify opportunities to automate repeatable work and improve team output.
+ Help shape practical AI workflows that support our workflows and initiatives.
Required Qualifications
- 4\+ years in sales enablement, product enablement, or a similar GTM role at a B2B SaaS tech company.
- Proven experience enabling field teams on complex or technical product capabilities.
- Ability to quickly develop deep product knowledge and translate it into seller\-ready content and programs.
- Direct facilitation and coaching experience with individual contributors.
- Strong project management skills; able to run multiple programs independently.
- Excellent communication and presentation skills for in\-person and virtual audiences.
Preferred Qualifications
- Experience enabling teams on AI, ML, or data\-driven product capabilities.
- Background in IT operations, observability, or infrastructure software.
- Familiarity with common sales platforms like Gong and Salesforce.
- Exposure to MEDDPICC, Command of the Message, or similar sales methodologies.
- Experience designing learning programs with defined evaluation criteria.
- Track record of adopting AI tools (e.g., Glean, ChatGPT, Claude) to drive measurable productivity gains for yourself or a team.
Why Join SolarWinds
- Be at the center of SolarWinds' most strategic growth initiative—AI\-powered IT operations—and shape how the field brings it to market.
- Work closely with Product and PMM at the intersection of product development and field readiness.
- Work within a collaborative enablement team where each person has a clear lane—you focus on AI GTM, not every function.
- An AI\-forward environment where new ways of working are actively built and adopted.
- A seat on a growing team, with scope to expand as SolarWinds' AI investment scales.
SolarWinds is an Equal Employment Opportunity Employer. SolarWinds will consider all qualified applicants for employment without regard to race, color, religion, sex, age, national origin, sexual orientation, gender identity, marital status, disability, veteran status or any other characteristic protected by law.
All applications are treated in accordance with the SolarWinds Privacy Notice: https://www.solarwinds.com/applicant\-privacy\-notice
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 SolarWinds, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
SolarWinds AI Hiring
SolarWinds has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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