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About This Role
At Quickbase, we’re on a mission to end a universal problem: Gray Work. The ad\-hoc, manual work we do looking for documents, resources, etc. when technology isn’t working for us—that’s Gray Work, and it negatively impacts productivity, employee well\-being and a company’s bottom line. Quickbase’s platform for Dynamic Work Management (the first ever) empowers nearly 12,000 organizations like Consigli, Suffolk, Valvoline, Daifuku and more to take on Gray Work by bringing people, processes, and data together into one central location, so employees can stop chasing information across systems and focus on work that makes an impact.
Our product is an AI\-powered platform that helps businesses connect scattered data, automate unique processes, and scale what makes them competitive. Instead of forcing you to change how you work to fit a rigid system, Quickbase adapts to your workflows—so you can eliminate manual workarounds, reduce errors, and get real\-time visibility across your operations.
Position Summary
Reporting to the CISO, the Senior AI Security Engineer will serve as a key contributor to Quickbase's AI security program.
This role is responsible for securing enterprise AI technologies, enabling the safe use of AI\-powered development tools, supporting AI governance initiatives, conducting AI risk assessments, and helping implement security controls that enable responsible AI adoption across the organization.
The ideal candidate combines strong cybersecurity fundamentals with practical experience working with generative AI technologies, developer AI tools, cloud\-native architectures, and modern software development practices.
Level \& Scope
This role:
- Operates independently across multiple AI security initiatives.
- Partners with Engineering, Product, IT, Legal, Privacy, Compliance, and business teams to support secure AI adoption.
- Provides security guidance for enterprise AI technologies, developer AI tooling, and AI\-enabled workflows.
- Supports implementation of AI governance requirements through technical controls and operational processes.
- Evaluates emerging AI technologies and recommends security best practices.
- Contributes to the evolution of QuickBase’s AI security capabilities, standards, and governance processes.
- Influences stakeholders and drives security outcomes without direct authority.
Key Responsibilities
AI Corporate Security
- Serve as the Security team's primary point of contact for enterprise AI technologies and internal AI adoption initiatives.
- Evaluate and secure the use of AI solutions across corporate functions including Engineering, Product, IT, Finance, HR, Customer Success, Sales, Marketing, and Legal.
- Assess security, privacy, compliance, and data protection risks associated with AI tools, AI assistants, copilots, AI agents, and AI\-enabled business workflows.
- Review AI integrations involving enterprise platforms such as GitHub, Salesforce, Slack, Jira, Confluence, SharePoint, Workday, ServiceNow, and other business\-critical systems.
- Define and implement security controls for AI\-enabled workflows, enterprise AI deployments, and agent\-based automation.
- Establish guardrails for the handling of sensitive, confidential, customer, employee, and regulated data within AI environments.
- Partner with IT and Security teams to improve visibility into AI usage, AI\-related risks, and AI adoption across the organization.
- Monitor emerging AI threats, vulnerabilities, and industry developments and recommend appropriate security controls and mitigations.
Developer AI Security
- Partner with Engineering teams to support secure adoption of AI\-assisted software development tools and workflows.
- Define security guardrails for AI code generation, code review, testing, and developer productivity tools.
- Review AI\-related engineering use cases and provide practical security recommendations.
- Develop standards and best practices for secure AI development.
- Support secure integration of AI capabilities into products and engineering workflows.
- Evaluate risks associated with AI\-generated code, AI agents, model integrations, and developer AI tooling.
- Collaborate with Product Security and Engineering teams to embed secure AI practices throughout the software development lifecycle.
AI Governance Enablement
- Partner with Security, Legal, Privacy, Compliance, and Engineering teams to implement AI governance requirements through technical controls and operational processes.
- Participate in AI review boards, architecture reviews, and AI technology assessments.
- Conduct AI risk assessments and document security, privacy, compliance, and operational risks.
- Support implementation of AI security standards, operational controls, and AI usage guardrails.
- Help establish visibility into AI adoption, usage patterns, and AI\-related risks across the organization.
- Contribute to AI metrics, reporting, and governance maturity initiatives.
- Assist with evaluating emerging AI regulations, industry guidance, and evolving best practices.
Security Automation \& Innovation
- Identify opportunities to leverage AI to improve security operations, engineering processes, and operational efficiency.
- Evaluate emerging AI security technologies and tooling.
- Develop automation solutions that improve visibility, governance, and security outcomes.
- Support initiatives involving AI agents, workflow automation, and security orchestration.
- Partner with Security Operations, IT, and Engineering teams on AI\-driven automation opportunities.
Qualifications
Required
- 4–7 years of experience in Security Engineering, Application Security, Product Security, Cloud Security, DevSecOps, Information Security, or related cybersecurity disciplines.
- Experience conducting security assessments, architecture reviews, technology evaluations, or risk assessments.
- Working knowledge of generative AI technologies, large language models (LLMs), AI agents, copilots, and AI\-powered development tools.
- Understanding of AI security risks including data leakage, prompt injection, excessive permissions, insecure outputs, model misuse, agent abuse, and emerging AI threats.
- Experience with cloud platforms such as AWS, Azure, and/or GCP.
- Familiarity with modern software development practices, APIs, CI/CD pipelines, and application security principles.
- Strong analytical, problem\-solving, communication, and stakeholder management skills.
- Ability to translate security requirements into practical and scalable solutions.
Preferred
- Experience supporting enterprise AI adoption, AI governance, AI risk management, or AI security initiatives.
- Experience with enterprise AI platforms such as ChatGPT Enterprise, Claude Enterprise, GitHub Copilot, Microsoft Copilot, Gemini, or similar technologies.
- Familiarity with AI governance frameworks and industry guidance such as NIST AI RMF, ISO 42001, OWASP Top 10 for LLM Applications, MITRE ATLAS, or responsible AI principles.
- Experience evaluating AI vendors, AI\-enabled SaaS platforms, or emerging technology solutions.
- Experience working in SaaS, cloud\-native, or high\-growth technology organizations.
At Quickbase, we believe in pay transparency and are committed to equitable pay practices. The compensation range for this role is $136,000 \- $210,000 per year. The exact compensation offered will be based on experience, skills, and alignment with internal equity. Beyond salary, employees receive bonus/commission eligibility and access to a full benefits package including health insurance, 401k, paid time off, etc.
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace.
Salary Context
This $136K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At QuickBase, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($173K) sits 5% below the category median. Disclosed range: $136K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
QuickBase AI Hiring
QuickBase has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $210K - $210K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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