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About This Role
We’re UserTesting—the leader in human insight. Our mission is to help organizations craft exceptional customer experiences through fast, actionable feedback.
We empower teams to build the best products and experiences by embedding real human perspectives into every stage of the development process—from ideation to launch. With the world’s strongest participant network, AI\-powered analysis, expert services, and seamless integrations, we help companies eliminate guesswork, align stakeholders, and bring customer needs into sharp focus.
Trusted by more than 3,000 organizations worldwide—including 75 of the Fortune 100—UserTesting delivers measurable business outcomes, reduces risk, and helps teams deliver with confidence. Joining our team means being part of a passionate group focused on transforming how companies understand and connect with their customers.
Let’s build experiences people love—together.
OVERVIEW
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We are seeking a highly skilled and proactive IT System Administrator with deep expertise in IT systems and a strong working knowledge of AI tooling and infrastructure. This role sits at the intersection of traditional enterprise IT administration and the rapidly evolving AI enablement space. You will play a hands\-on IC role in deploying and maintaining the AI tools, agents, and infrastructure that support UserTesting’s broader AI initiatives, along with other more traditional IT responsibilities. Target ratio is a 80/20 split of AI “building” vs Traditional SysAdmin work..
Key Responsibilities
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AI Enablement \& Infrastructure
- Set up, configure, and maintain MCP (Model Context Protocol) servers that connect AI systems to internal tools and data sources, including scoping access, managing credentials, and troubleshooting integrations.
- Enable and manage AI features within existing SaaS platforms (e.g., Atlassian Rovo, Slack AI, Okta AI, ChatGPT, Google Gemini), working with vendors and internal stakeholders to configure, roll out, and govern usage.
- Provision and support AI agent infrastructure: service accounts, API key management, permissions scoping, rate limit monitoring, and access auditing for agents operating across internal systems.
- Support the deployment and lifecycle management of AI solutions. Heavy importance on project/process management and communication skills for scoping calls, stakeholder meetings, and demo\-style calls.
- Maintain documentation for all AI integrations, including architecture diagrams, access maps, runbooks, and troubleshooting guides.
Stay current on the AI tooling ecosystem and proactively identify tools or integrations that could benefit the IT team or the broader organization.
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System Administration and Maintenance
- Support the administration of UserTesting’s IT systems and SaaS toolset across the organization.
- Become a subject matter expert for AI\-enabled platforms, integrations, and supporting infrastructure.
- Maintain assigned platforms through onboarding, offboarding, access management, feature rollouts, system cleanups, and lifecycle reviews.
- Identify, prioritize, and address technical debt across IT systems, workflows, and integrations.
Partner with other IT System Administrators to improve reliability, automation, documentation, and supportability.
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Support \& Collaboration
- Act as an escalation point for complex ITSM issues for the IT support team.
- Collaborate with cross\-functional teams (Security, People Ops, Engineering, etc) to improve onboarding/offboarding, lifecycle processes, and AI tool adoption.
Document procedures, system changes, and troubleshooting workflows.
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Job Description
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The IT team at UserTesting manages company\-wide systems and serves as an integration, automation, and support layer for the company’s many technology initiatives. This role extends that mission into the AI space. An ideal candidate is someone who can seamlessly transition between managing existing infrastructure and configuring AI tools and systems like custom MCPs and complex agent based solutions.
The person in this role doesn’t need to be a traditional developer, but must be comfortable working in technical environments: reading API documentation, managing credentials and service accounts, troubleshooting connectivity between systems, writing scripts to automate routine work, and thinking critically about how AI tools interact with the rest of the company's tech stack.
Role Requirements
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- Able to work Monday through Friday in the US Eastern or Central time zones.
- 5\+ years of experience with enterprise IT systems in a system administrator capacity.
- Hands\-on experience with MCP server setup and management, AI agent deployment, AI feature configuration within enterprise SaaS tools.
- Familiarity with scripting \& automation technologies in Python and/or Bash, with working knowledge of Git for version control and JSON/YAML for config and API work.
- Familiarity with containerization (Docker), including running and troubleshooting containerized services — experience with MCP servers or similar tooling a plus.
- Familiarity with API\-based integrations — managing keys, scoping permissions, reading docs, and troubleshooting connectivity issues.
- Ability to work in a Kanban\-style environment, managing due dates, new tasks, and tech debt effectively.
- Ability to train and mentor IT Support staff on ITSM practices as they apply to your role and function.
- Familiarity with IDP tools like Okta, as well as the concepts of OAuth and SSO from an IT perspective.
- Certifications in AI platforms are a plus (e.g., AWS, Google Cloud, Anthropic), but demonstrated hands\-on experience is valued more highly, be prepared to explain this experience with real world examples.
- Ability to learn in a constantly shifting environment. We will do our best to manage processes, but the field of AI is new and growing. Change is unavoidable.
Don’t meet every single requirement but excited about the role? We encourage you to apply. Research shows that some candidates are less likely to apply unless they meet 100% of the qualifications—but you may be exactly who we’re looking for. Diverse perspectives drive innovation, and we’re committed to building a team with a wide range of backgrounds, experiences, and skills.
Application Process
- Meet with a Recruiter
- Meet the Hiring Manager
- Participate in a Panel Interview
- Final Interview (if applicable)
- Offer Stage
Accommodations
At UserTesting, we’re committed to creating inclusive and accessible experiences for all candidates. We believe diverse perspectives are essential to building exceptional products and experiences. If you require accommodations or would like us to tailor the interview process to better meet your needs, please contact us at [email protected] .
UserTesting is an Equal Opportunity Employer and a participant in the U.S. Federal E\-Verify program. Women, minorities, individuals with disabilities and protected veterans are encouraged to apply. We welcome people of different backgrounds, experiences, abilities and perspectives. UserTesting will consider qualified applicants with criminal histories in a manner consistent with the San Francisco Fair Chance Ordinance, as applicable.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At UserTesting, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
UserTesting AI Hiring
UserTesting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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