Director, Product Management - AI

$216K - $236K Remote Mid Level AI/ML Engineer

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Skills & Technologies

AwsAzureClaudeGcpGeminiPython

About This Role

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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

Director of Product Management, AI

Location: West Coast, US (Seattle preferred)

Department: Product Management | Reports to: SVP of Product

The Opportunity

Alteryx is the agentic analytics and automation company that powers actionable insights for more than 8,000 customers worldwide — organizations that together run over 380 million automated workflows a year on the Alteryx platform. We're looking for a Director of Product Management, AI to own one of the most important stories in the company: proving, in hard numbers, why Alteryx plus large language models beats either one alone.

This isn't a research role for AI's own sake. It's a product leadership role focused on ROI — building and articulating the value proposition that governed Alteryx workflows make AI faster, better, and cheaper than raw LLM usage, and then shipping the product capabilities that make that promise true for every customer.

Every enterprise is asking the same question about generative AI: is it actually paying off? Alteryx has the receipts. When an LLM operates on raw, ungrounded data, it guesses at business rules and burns tokens doing it. When that same LLM is paired with a trusted, governed Alteryx workflow, the results change dramatically.

This is the Faster / Better / Cheaper equation you'll own: Faster, because governed workflows resolve in a fraction of the time raw LLM reasoning takes. Better, because every answer is Visible, Understandable, Repeatable, and Auditable (Alteryx calls this VURA) instead of a one-off, unverifiable guess. Cheaper, because customers stop paying the “re-work tax” of rebuilding business logic and re-burning tokens every time they plug in a new agent or model.

What You'll Do

  • Build the products that make “build once, govern once” real — extending governed workflows, datasets, and business logic into the AI agents and LLMs teams already use, including surfaces like Agent Studio, the Alteryx MCP Server, Ask Alteryx, and emerging integrations with ChatGPT, Claude, Gemini, Slack, and Microsoft Teams.
  • Define and prioritize the roadmap for grounding LLMs in governed, trusted business logic so every AI-driven action is as accurate and compliant as a human-executed workflow.
  • Partner with Engineering, Data Science, and the CPO organization to benchmark, measure, and continuously improve the cost and speed advantage Alteryx delivers over standalone LLM usage.
  • Work directly with enterprise customers and analysts to quantify AI ROI in their environments.
  • Represent the AI product vision externally: analyst briefings (Gartner, IDC, Forrester), customer advisory boards, and public launches.
  • Champion the VURA framework (Visible, Understandable, Repeatable, Auditable) as the standard for what “trustworthy AI” means at Alteryx, and make sure it shows up in every feature you ship.

What You'll Bring

  • 10+ years in product management, with meaningful time spent leading AI, ML, or data-platform products at a B2B SaaS or enterprise software company.
  • A track record of building and defending an ROI or TCO narrative — you're as comfortable in a spreadsheet proving cost-per-token savings as you are on stage presenting them.
  • Working knowledge of how LLMs, agents, and retrieval/grounding actually behave in production, including where they're expensive, where they hallucinate, and how governed data and business logic fix both.
  • Experience shipping products that integrate with or extend into third-party AI ecosystems (MCP, ChatGPT plugins, Copilot, Claude, Gemini, or similar).
  • Strong cross-functional leadership: you can align engineering, data science, sales, and analyst relations around a single, numbers-backed story.
  • Excellent written and verbal communication — this role is as much about telling the ROI story as building it.
  • Bachelor's degree in a related field required; MBA or technical graduate degree a plus.

Location

This role is remote based on the West Coast, with a strong preference for Seattle.

Senior Security Operations Center (SOC) Analyst

Alteryx is searching for a Senior Security Operations Analyst in the United States. We’re looking for problem solvers, innovators, and dreamers who are searching for anything but business as usual. Like us, you’re a high performer who’s an expert at your craft, constantly challenging the status quo. You value inclusivity and want to join a culture that empowers you to show up as your authentic self. You know that success hinges on commitment, that our differences make us stronger, and that the finish line is always sweeter when the whole team crosses together.

Position Overview

As a member of the Security Operations team, you will be on the front line of protecting Alteryx products, infrastructure, and applications. You will triage and investigate alerts, lead incident response activities, and improve detection coverage and response outcomes across endpoint, identity, SaaS, and multi-cloud environments (AWS, Azure, GCP). This role is SOC-focused and is intended for a Senior level analyst with demonstrated depth in one or more of the following areas: Forensics, Cloud Security Investigations (AWS/Azure/GCP audit logs and cloud-native security signals), Threat Hunting, and Detection Engineering.

Primary Responsibilities

  • Triage and respond to security alerts and incidents across on-prem and multi-cloud enterprise and product environments, leading in-depth investigations using SIEM, EDR, cloud audit logs, identity telemetry, and network data to determine scope, root cause, attacker TTPs, and business impact.
  • Lead incident communications (severity updates, stakeholder coordination, executive-ready summaries as needed)
  • Execute incident response activities through containment and remediation coordination with partner teams (IT, Cloud/Platform, Engineering), including clear escalation when needed.
  • Produce high-quality incident documentation (timelines, evidence collected, hypotheses tested, IOCs, actions taken, lessons learned) and ensure follow-ups are tracked to completion.
  • Conduct hypothesis-driven threat hunts mapped to common adversary behaviors (e.g., MITRE ATT&CK).
  • Translate hunt findings into actionable improvements: new detections, tuning, playbooks, and telemetry/visibility recommendations.
  • Develop, tune, and maintain detection content (correlation rules, SIEM analytics, alert logic) to improve coverage and reduce false positives.
  • Validate detections with testing and retrospective analysis; continuously improve alert fidelity and response workflows.
  • Collaborate with stakeholders to define and maintain monitoring and detection use cases that drive risk reduction and operational effectiveness.
  • Perform basic endpoints and/or cloud forensics during escalated incidents and preserve evidence appropriately.
  • Support malware triage and artifact analysis as needed during investigations.
  • Investigate cloud-related threats and anomalies using cloud-native security signals and audit telemetry (AWS/Azure/GCP).
  • Partner with Cloud/Platform teams to close investigation gaps (logging, retention, telemetry coverage) and validate remediation actions.
  • Participate in an on-call rotation to provide 24x7 incident response coverage and serve as an escalation point for high-severity events.

Required Qualifications

  • Bachelor’s degree in computer science or related field (or equivalent experience/training). A master’s degree is a plus.
  • 4+ years of relevant security operations / incident response experience
  • Strong understanding of the security incident management lifecycle and operational response practices.
  • Strong experience with SIEM/log management platforms (e.g., Microsoft Sentinel, Splunk, ELK, Snowflake-based analytics, or similar) and demonstrated ability to query and analyze telemetry.
  • Ability to analyze and interpret security-relevant data including security event logs, system logs, application logs, cloud logs, and device logs.
  • Hands-on investigation experience using cloud-native security services (e.g., AWS Security Hub, Microsoft Defender for Cloud, Google Security Command Center) and cloud logs.
  • Experience with one or more scripting languages (Python, PowerShell, shell) to support investigations, enrichment, and analysis.
  • Strong written and verbal communication skills, including clear ticket documentation, incident reporting, and stakeholder updates.
  • Excellent analytical and problem-solving skills with a bias toward action and operational rigor.

Preferred Qualifications (One or More Areas of Depth)

  • Forensics: Endpoint or cloud forensics, evidence handling, artifact interpretation, malware triage.
  • Threat Hunting: Demonstrated experience leading hypothesis-driven hunts and operationalizing results into detections, playbooks, and response improvements (e.g., ATT&CK mapping and coverage tracking).
  • Detection Engineering: Detection lifecycle ownership (build/test/deploy/tune/retire), alert quality improvement, playbook development.

Alteryx is committed to fair, equitable, and transparent compensation. Final compensation will be determined by various factors such as your relevant work experience, education, certifications, skills, and geographic location.

The salary range for this role in the United States is $216,000 - $236,000.

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.

About Alteryx

Alteryx is a leading AI-ready data and analytics company that powers actionable insights to help organizations drive smarter, faster decisions with data. More than 8,000 customers around the world rely on Alteryx to automate analytics, improve revenue performance, manage costs, and mitigate risk across their businesses. Alteryx provides the business logic layer for enterprise AI — connecting the AI tools and agents organizations use to the governed workflows, datasets, and analytics they already rely on — helping organizations move beyond AI experimentation toward AI that is trusted, actionable, and built for ROI.

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 $216K-$236K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Company Alteryx
Title Director, Product Management - AI
Location Remote, US
Category AI/ML Engineer
Experience Mid Level
Salary $216K - $236K
Remote Yes

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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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

Aws (9% of roles) Azure (8% of roles) Claude (4% of roles) Gcp (5% of roles) Gemini (2% of roles) Python (17% of roles)

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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($226K) sits 7% above the category median. Disclosed range: $216K to $236K.

Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.

Alteryx AI Hiring

Alteryx has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $132K - $236K.

Remote Work Context

Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.

The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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

Based on 11,857 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,653 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Alteryx is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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