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About This Role
Labcorp is a global leader in laboratory services, providing the insights and answers that help healthcare providers, patients, researchers, pharmaceutical companies and health systems make confident decisions and improve outcomes. Through our unparalleled science, data, technology and laboratory network, we advance diagnostics, accelerate innovation and help address some of the world’s most important health challenges. As we shape the future of healthcare, we are leveraging advanced technologies, intelligent digital solutions and data\-driven innovation across our operations to enhance how work gets done and deliver greater value to customers and patients. With our global scale and deep expertise, you’ll have the opportunity to do meaningful work, grow your career and make a real impact on people’s health around the world. Together, we’re improving health and improving lives.
Labcorp is a global leader in diagnostic testing and drug development solutions, helping healthcare providers, researchers, and patients make informed decisions that advance care. Join us in our mission to improve health and improve lives.
Labcorp is seeking an IT Operations \& AI Performance Analyst I to join our team in Durham, North Carolina.
Work Schedule: This is a full‑time, exempt (salaried) position assigned to a First Shift schedule, with standard business hours of Monday through Friday, 8:00 a.m. to 5:00 p.m. in your local time zone. Business needs may occasionally require flexibility in work hours, including earlier, later, or additional hours, with reasonable notice provided when possible.
Applicants who live within 35 miles of either the Burlington, NC or Durham, NC location will follow a hybrid schedule. This schedule includes a minimum of three in\-office days per week at an assigned location, either Burlington or Durham, supporting both collaboration and flexibility.
Job Responsibilities:
AI\-First Analytics \& Automation
- Use approved AI tools such as Replit, Claude Code, and Cursor to explore IT service data, spot patterns, and identify opportunities to improve reporting, automation, and service quality
- Build and maintain automation workflows with AI\-assisted development tools to reduce manual data collection and make recurring reports more reliable
- Apply AI assistance to documentation, data review, and analysis, validating outputs for accuracy and business context
Dashboard \& Visualization Development
- Use AI\-assisted development to design, build, and maintain Power BI and Microsoft Fabric dashboards that show system uptime, incident resolution time, SLO/SLA compliance, service health, and security\-related measures
- Translate business and technical requirements into clear, actionable visuals that enable technical teams, managers, and leaders to easily understand and make informed decisions.
Data Preparation \& Reporting
- Collect, clean, validate, and prepare data from ServiceNow, CMDB, monitoring tools, and other IT systems for reports and dashboards
- Support ServiceNow reporting for the IT Operational \& Performance team by creating and maintaining reports, dashboard widgets, and metrics for incident, change, request, and service performance processes
- Help maintain accurate data, refresh schedules, and documentation for recurring reports and dashboards
Collaboration \& Storytelling
- Partner with the Enterprise Data \& Analytics team to align on approved data sources, definitions, and reporting standards
- Participate in meetings with Infrastructure, Cybersecurity, and Application teams to review performance measures, answer questions, and capture follow\-up actions
- Help prepare summaries and presentations that explain trends, risks, and improvement opportunities in a clear way
- Document report logic, metric definitions, KPI, data sources, assumptions, and automation steps so others can understand and maintain the work
Minimum Qualifications:
- Bachelor's degree in Computer Science, Information Systems, Data Analytics, Statistics
- Current or previous internship completed at Labcorp
Preferred Qualifications:
- 1\-2 years exposure to AI or machine learning concepts through coursework, projects, certifications, hands\-on practice, or applied use of AI tools
- 1\-2 years experience programming or scripting experience in Python, PowerShell, JavaScript, or a similar language for data analysis or automation
- 1\-2 years exposure to Microsoft Fabric tools such as Dataflows, Lakehouse, data pipelines, or Synapse Analytics
- 1\-2 years experience with ServiceNow experience or understanding of ITSM and ITIL practices, including incident, change, request, or problem management
- 1\-2 years experience using data analysis and visualization tools (Replit, pandas, matplotlib, Plotly, or similar) to turn data into clear insights that support decision\-making
- 1\-2 years experience with IT operations measures such as;incident volume, SLA tracking, system availability, backlog, or monitoring alerts
- 1\-2 years of experience in IT operations, analytics, business intelligence, service management, automation, or a related technical area
Additional Job Standards:
AI Productivity \& Collaboration Tools
- Familiarity with AI development and productivity tools such as Replit, Claude Code, Cursor, Microsoft Copilot, GitHub Copilot, or similar approved tools
- Ability to use AI tools responsibly to support research, documentation, code review, automation, and analysis while checking work for accuracy
- Basic understanding of version control and collaboration workflows in Git or GitHub
- Willingness to learn new AI, analytics, automation, and development tools as business needs change
Technical Skills
- Working knowledge of Microsoft Power BI, including Power Query and basic DAX for data modeling and calculations
- Strong Microsoft Excel skills, including formulas, pivot tables, charts, and data cleanup
- Basic SQL knowledge for finding, filtering, and joining data
- Understanding of good dashboard design, data quality, and how to present metrics clearly
Mindset \& Approach
- Strong analytical and problem\-solving skills with attention to detail and data accuracy
- Curiosity about AI\-first ways of improving IT operations, automation, reporting, and service quality
- Ability to learn quickly, ask thoughtful questions, and apply feedback
- Clear written and verbal communication skills for both technical and non\-technical audiences
- Well\-organized and able to manage multiple priorities, deadlines, and recurring reporting activities
AI \& Automation
- Replit, Claude Code, and Cursor for AI\-assisted development and automation
- Microsoft Copilot, GitHub Copilot, and other approved AI productivity tools
- Data analysis and visualization libraries for reporting and automation
Core Platforms
- ServiceNow (reports, dashboards, ITSM data)
- Microsoft Excel (data cleanup, analysis, charts)
- SQL databases and query languages
- Microsoft Power BI (Power Query, DAX, data modeling, dashboards)
- Microsoft Fabric (Dataflows, Lakehouse, data pipelines)
Collaboration \& Documentation
- Bitbucket or GitHub for version control and collaboration
- Markdown, documentation tools, and standard operating procedures
The IT Operations \& AI Performance Analyst is a full\-time role for a motivated technology professional who thinks AI first about reporting, data visualization, automation, and service improvement. This role uses approved AI tools, data, and dashboards to help IT teams understand how services are performing and where improvements are needed across infrastructure, cybersecurity, applications, and service delivery.
Working within the IT Operations \& Performance Management team, you will use AI\-assisted approaches to build dashboards, maintain recurring reports, reduce manual work, and document key performance measures. You will gain hands\-on experience with AI development tools such as Replit, Claude Code, and Cursor, alongside Power BI, Microsoft Fabric, ServiceNow, Excel, and SQL.
This role is a good fit for someone with a solid technical foundation, strong attention to detail, and curiosity about using AI responsibly in everyday IT work. You will work with senior team members and business partners to turn technical information into clear, useful insights that support better decisions.
This position operates in a collaborative, fast\-paced IT environment where AI\-enabled thinking, clear reporting, quality, and continuous improvement are important. You will work with cross\-functional teams including Infrastructure, Cybersecurity, Application Development, Enterprise Data \& Analytics, and Business Leadership stakeholders.
What You'll Gain
- Hands\-on experience applying AI productivity tools to enterprise analytics, reporting, automation, and IT performance work
- Exposure to large\-scale IT operations and performance management
- Mentorship from senior analytics professionals and technical leaders
- Opportunity to help improve how IT teams use AI, data, and automation to work more effectively
- A career path toward analyst, automation, data, or IT operations roles
- Work on visible projects that help improve IT service quality and business outcomes
Reports To
Executive Director, IT Operations \& Performance Management
Benefits:
Employees regularly scheduled to work 20 or more hours per week are eligible for comprehensive benefits including: Medical, Dental, Vision, Life, STD/LTD, 401(k), Paid Time Off (PTO) or Flexible Time Off (FTO), Tuition Reimbursement and Employee Stock Purchase Plan. Employees regularly scheduled to work less than 20 hours, Casual, Intern, and Temporary employees are only eligible to participate in the 401(k) Plan. Employees who are regularly scheduled to work a 7 on/7 off schedule are eligible to receive all the foregoing benefits except PTO or FTO. For more detailed information, please click here.
Labcorp is proud to be an Equal Opportunity Employer:
Labcorp strives for inclusion and belonging in the workforce and does not tolerate harassment or discrimination of any kind. We make employment decisions based on the needs of our business and the qualifications and merit of the individual. Qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), family or parental status, marital, civil union or domestic partnership status, sexual orientation, gender identity, gender expression, personal appearance, age, veteran status, disability, genetic information, or any other legally protected characteristic. Additionally, all qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law.
We encourage all to apply
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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 Labcorp, 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.
Labcorp AI Hiring
Labcorp has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Durham, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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