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About This Role
Meet the Moment with Alteryx
We're living through a once\-in\-a\-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision \- and Alteryx is leading the transformation.
You'll be working on the challenges that sit at the heart of modern business. No matter your role, the work you do will help organizations move faster, see more clearly, and tackle questions that used to feel impossible.
If you're ready to meet the moment with innovation, curiosity, and excellence, there's a place for you here.
Alteryx is seeking an AI\-forward Senior Director to lead the transformation of our GTM processes and systems. Reporting directly to the SVP of Revenue Operations, this people\-leadership position will be the driving force to leverage AI, automation, and process re\-engineering to eliminate bottlenecks to GTM productivity, reduce the elapsed time required to complete key GTM activities, and optimize GTM engagements with customers and partners.
The scope of this people\-leadership position spans from the identification of the best opportunities that accelerate our GTM strategy from a sea of “great ideas” to deploying \& maintaining high\-impact solutions at scale. The ideal candidate will be a hands\-on operator with the skills and experience to perform the necessary work themselves, as well as through directing the onshore/offshore resources that report to this position or by collaborating with developers/IT colleagues in other teams.
Primary Responsibilities:
- Lead a team of onshore and offshore colleagues that are the business owners of the applications and technology used by the GTM organization.
- Drive the transformation, rationalization, and integration of the existing GTM technology landscape with an AI\-first mentality to enable acceleration of the Alteryx GTM strategy.
- Partner closely with GTM management to deeply understand existing system and process challenges to execution of the Alteryx GTM strategy.
- Act as a trusted advisor to GTM leadership to advise on the best opportunities for the use of AI and translate AI capabilities into business value.
- Define and evolve standardized end\-to\-end GTM processes that serve as the basis to streamline execution through automation, integrations between systems, and the use of AI.
- Identify, prioritize, and re\-engineer slow, inefficient, and/or people\-intensive GTM processes using an AI\-first mentality to increase productivity and reduce elapsed time.
- Design new solutions, define enhancements to current systems, and/or select off\-the\-shelf applications to automate and accelerate GTM processes. Incorporate AI to increase ROI by improving GTM productivity, accelerating GTM time\-to\-value, and/or optimizing GTM engagements.
- Define integrations between existing GTM systems and applications that streamline and simplify the execution of optimized GTM processes and ensure data consistency across systems. Incorporate the use of AI agent where appropriate.
- Collaborate with IT or other development teams to develop, deploy, and maintain these new solutions, enhancements, applications, and integrations to facilitate existing \& re\-engineered GTM processes, automate manual work, and streamline execution.
- Lead and direct your team (without other IT or development teams) in identifying, prioritizing, harvesting, deploying at scale, and maintaining LLMs, agents, and other AI solutions developed by individual GTM colleagues that can increase the performance and/or productivity of large groups of GTM colleagues.
- Partner with GTM Enablement and GTM Communications to ensure the successful rollout and adoption of new GTM solutions, enhancements, and AI capabilities. Serve as the subject matter expert to provide and edit content for communications, training, and ongoing reference as needed.
- Provide leadership and guidance to the overall GTM organization in the use of artificial intelligence such as tips for AI prompts, high\-value GTM use cases for using Gemini, etc.
- Collaborate with peers in other Alteryx departments (Marketing, Products, etc.) to ensure alignment in the development and evolution of AI solutions, share AI knowledge and tips, and tackle cross\-departmental challenges that should be addressed with AI.
Required Skills:
- 10\+ years of experience in GTM operations at a high\-tech company, including significant experience re\-engineering GTM processes. Broad experience across GTM operations functions is preferred.
- 5\+ years of direct management experience of GTM operations teams. Experience in building new teams and managing both on\-shore and off\-shore teams is strongly preferred.
- 5\+ years of business owner experience for the GTM technology landscape with proven success rationalizing and transforming systems.
- 3\+ years deep, hands\-on experience developing with AI tools, scaling AI\-based solutions, and managing modern technology stacks, combined with the operational pragmatism to know when and how to deploy them effectively. Experience managing AI developers is strongly preferred.
- Exceptional communication (verbal and written) and collaboration skills with executives, sales managers, sales reps, cross\-departmental leaders, development teams and IT.
- Executive presence. High comfort level and proven success in presenting to and advising business executives.
- Advanced ability to drive and manage through change.
- Team player who contributes significant value in cross\-functional initiatives as a leader or team member.
- A Bachelor of Science (BS) degree in a technical field, such as Computer Science, Computer Engineering, or Data Science/Intelligence.
Valued Skills:
- Proven success in rationalizing, optimizing, and integrating GTM system landscapes; decommissioning legacy GTM systems; and automating manual work to create streamlined operational workflows for a GTM organization.
- Previous experience working as a quota\-carrying Account Executive, Sales Manager, or Solution Engineer is a plus.
- Experience operating in a matrixed, global environment.
Compensation:
Alteryx is committed to fair, equitable, and transparent compensation.
250,000\-290,000 plus Bonus \& Equity
Final compensation will be determined by various factors such as your relevant work experience, education, certifications, skills, and geographic location.
Employees may also be eligible for a wide range of other benefits, such as a bonus or commission, medical, retirement, financial, wellness, time off, employee discounts, and others.
Find yourself checking a lot of these boxes but doubting whether you should apply? At Alteryx, we support a growth mindset for our associates through all stages of their careers. If you meet some of the requirements and you share our values, we encourage you to apply. As part of our ongoing commitment to a diverse, equitable, and inclusive workplace, we’re invested in building teams with a wide variety of backgrounds, identities, and experiences .
Benefits \& Perks:
Alteryx has amazing benefits for all Associates which can be viewed here .
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Alteryx will consider for employment qualified applicants with arrest and conviction records.
This position involves access to software/technology that is subject to U.S. export controls. Any job offer made will be contingent upon the applicant’s capacity to serve in compliance with U.S. export controls.
Salary Context
This $250K-$290K range is above the 75th percentile 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 Alteryx, 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 ($270K) sits 23% above the category median. Disclosed range: $250K to $290K.
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.
Alteryx AI Hiring
Alteryx has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in CA, US. Compensation range: $290K - $290K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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