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About This Role
About MasterControl:
MasterControl Inc. is a leading provider of cloud\-based quality and compliance software for life sciences and other regulated industries. Our mission is the same as that of our customers to bring life\-changing products to more people sooner. The MasterControl Platform helps organizations digitize, automate and connect quality and compliance processes across the regulated product development life cycle. Over 1,000 companies worldwide rely on MasterControl solutions to achieve new levels of operational excellence across product development, clinical trials, regulatory affairs, quality management, supply chain, manufacturing and postmarket surveillance. For more information, visit www.mastercontrol.com.
Summary
We are seeking an innovative and AI\-driven Applied AI Solutions Engineer to join our internal Data \& AI team. This role is at the forefront of how our organization leverages artificial intelligence — from building intelligent data pipelines and AI\-powered workflows to designing dashboards that surface both AI\-generated insights and critical business metrics. Partnering with stakeholders across multiple business units, you will be a key driver in shaping our AI foundation, working hands\-on with cutting\-edge tools like Claude Code to automate processes, engineer prompts, and deliver real business value through applied AI/data solutions.
Responsibilities
- Design, develop, and deploy AI/Data workflows and solutions using Claude Code, LLMs, and prompt engineering to automate internal processes and solve complex business challenges
- Engineer, manage, and version\-control AI prompts, incorporating stakeholder feedback to continuously improve AI performance and accuracy
- Connect to and integrate data from a variety of internal and external sources, building and maintaining SQL transformation pipelines to prepare, clean, and structure data for AI consumption and business reporting
- Partner with cross\-functional team members and executives to understand business needs, identify and prioritize AI use cases, and define reporting solutions (Domo KPIs and dashboards) that support data\-driven decision\-making
- Troubleshoot and support internal AI platforms, tools, and data pipelines
- Document solution designs, prompt configurations, data flows, and implementation processes to support knowledge transfer and transparency
- Stay current with emerging AI tools, models, and best practices to continuously bring new ideas and capabilities to the team
Required Skills
- Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Statistics, Information Systems, or a related field
- Demonstrated hands\-on experience with Large Language Models (LLMs), including prompt engineering, model optimization, and AI workflow development
- Proficiency in SQL — experience with any flavor of SQL is welcome (e.g., MySQL, PostgreSQL, Redshift, SQL Server, etc.), with working knowledge of MySQL and Amazon Redshift being a plus
- Experience working with Claude or similar LLM platforms, including Claude Code for AI\-driven development
- Hands\-on experience with Domo or comparable BI and data visualization platforms, including dashboard design and KPI tracking
- Ability to work closely with stakeholders to gather requirements and translate business needs into clear, effective data visualizations
- Strong analytical and problem\-solving skills with close attention to detail
- Excellent communication skills with the ability to explain AI concepts and data insights clearly to non\-technical stakeholders
- Genuine passion for AI, automation, and finding practical AI applications that drive real\-world impact
Preferred Qualifications
- Experience developing and deploying AI\-powered applications or internal AI products
- Familiarity with AI frameworks such as TensorFlow, PyTorch, or Hugging Face
- Experience integrating AI solutions with enterprise systems via APIs or data pipelines
- Knowledge of containerization technologies (Docker, Kubernetes)
- Background in a consulting, professional services, or stakeholder\-facing technical role
- Project management experience or certification
- Working knowledge of AWS cloud services, particularly those relevant to AI/ML deployments
About the Role
This is a rare opportunity to be at the center of AI innovation from the inside out. You won't just be supporting AI — you'll be building it, applying it, and continuously improving it. At the same time, you'll play a meaningful role in helping the broader business understand its data through well\-crafted dashboards and reporting. The ideal candidate is deeply curious about what AI can do, comfortable working with the latest LLM tools, and excited to bring that energy to a collaborative, fast\-moving team
\#WhyWorkAnywhereElse?
MasterControl is a place where Exceptional Teams come together to do their best work. In fact, hiring Exceptional Teams is a core value of ours. MasterControl employees are surrounded by intelligent, motivated, and collaborative individuals. We like to call it \#TheBestTeamOnThePlanet.
We work hard to develop and challenge our employees' skillsets, recognize their contributions, encourage professional development, and offer a one\-of\-a\-kind culture. This is why we say \#WhyWorkAnywhereElse?
MasterControl could be your next (and last) career move!
Here are some of the benefits MasterControl employees enjoy:
- Competitive compensation
- 100% medical premium coverage (yes, you read that right!)
- 401(k) plan with company match
- Generous PTO packages that increase with tenure
- Schedule flexibility
- Onsite physician and massage therapist
- Dental/vision plans
- Employer paid life insurance policy
- Much, much more!
Applicants must be currently authorized to work in the United States on a full\-time basis.
*The US base salary range for this full\-time position is $75,000 \- $100,000 \+ benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.*
*MasterControl is an Equal Opportunity Employer. If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, you may contact [email protected] or call (801\) 942\-4000 and ask to speak with a member of Human Resources.*
*Equal Opportunity Employer, including disability and protected veteran status*
Salary Context
This $75K-$100K range is in the lower quartile 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 MasterControl, 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. This role's midpoint ($87K) sits 60% below the category median. Disclosed range: $75K to $100K.
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.
MasterControl AI Hiring
MasterControl has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US. Compensation range: $100K - $100K.
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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