Interested in this AI/ML Engineer role at WebMD?
Apply Now →About This Role
WebMD Ignite, a division of WebMD and Internet Brands, is the growth partner for healthcare organizations. We guide people to better health at all stages of their journey, from discovery to recovery. Our combination of leading brands in the industry—including WebMD, Medscape, Krames, PulsePoint, Vitals, The Wellness Network, Mercury Healthcare, and Healthwise—offers comprehensive solutions that engage individuals with timely, relevant messaging that optimizes experiences and outcomes, driving loyalty and lifetime value for our clients. Learn more at WebMDIgnite.com.
WebMD Ignite, a division of WebMD and Internet Brands, is the growth partner for healthcare organizations. We guide people to better health at all stages of their journey, from discovery to recovery. Our combination of leading brands in the industry — including WebMD, Medscape, Krames, PulsePoint, Vitals, The Wellness Network, Mercury Healthcare, and Healthwise — offers comprehensive solutions that engage individuals with timely, relevant messaging that optimizes experiences and outcomes, driving loyalty and lifetime value for our clients. Learn more at WebMDIgnite.com.About the Role:
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The Sr. Manager, Test Automation \& AI Evaluation is an automation\-first quality leader responsible for quality engineering across the Ignite Growth Platform. As engineering velocity has accelerated with AI\-assisted development, quality assurance has become the rate\-limiting constraint; this role exists to remove that constraint. The Sr. Manager makes automation the default, builds the evaluation discipline required to test AI\-powered and agentic features, and applies AI to the practice of QA itself. This role also provides direct leadership of the existing Educate quality assurance team and serves as the head of quality assurance across Ignite, owning quality standards, test strategy, and release\-readiness governance organization\-wide.Key Responsibilities:
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- Lead an automation\-first quality strategy, eliminating manual testing waste and making automated testing the default across teams.
- Own modern test automation frameworks and end\-to\-end automated coverage, with deep, current expertise in Playwright and comparable frameworks.
- Build and own evaluation frameworks for LLM\-powered features and agentic systems, covering correctness, hallucination, prompt drift, and behavioral regression.
- Establish and maintain evaluation methodology such as curated golden sets, LLM\-as\-judge approaches calibrated against subject\-matter experts, and continuous learning loops that feed production failures back into the benchmark.
- Apply AI to QA itself, including self\-healing automation, AI\-assisted test generation, and intelligent defect triage.
- Own standardization of quality engineering practices across the organization, driving accountability for test coverage, defect management, release readiness, and overall delivery quality.
- Provide direct management, mentorship, and structure to the existing Ignite quality assurance function.
- Support the development of a scalable Quality Management System to ensure consistency, traceability, and process rigor across teams.
- Partner with engineering, product, and DevOps to define acceptance criteria and embed quality gates into the delivery lifecycle.
Qualifications:
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- 7\+ years of experience in quality engineering or test automation, including team leadership or management.
- Deep, current, hands\-on expertise in modern test automation frameworks such as Playwright, Cypress, or Selenium.
- Demonstrated experience building automated test suites and integrating them into CI/CD pipelines.
- Experience designing evaluation or testing approaches for AI/ML or LLM\-powered systems, or a strong data\-and\-metrics orientation with the ability to build one.
- Strong understanding of test strategy, defect management, and release\-readiness governance across multiple teams.
- Proven ability to lead, mentor, and grow a quality engineering team.
- Excellent communication skills and the ability to translate quality risk into business terms.
Preferred Skills:
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- Experience testing generative AI or agentic products, including evaluation harnesses, prompt testing, and regression testing for AI flows.
- Experience in a healthcare, health IT, or other regulated delivery environment where quality standards are elevated.
- Familiarity with quality management systems and traceability requirements.
- Experience with AI\-assisted testing tooling and self\-healing automation platforms.
Compensation: $98,000 \- $135,000
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Bonus Eligible: This position is also eligible for a discretionary company bonus, based upon business results.
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Benefits: Employees in this position are eligible to participate in the company sponsored benefit programs, including the following within the first 12 months of employment:
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- Health Insurance (medical, dental, and vision coverage)
- Paid Time Off (including vacation, sick leave, and flexible holiday days)
- 401(k) Retirement Plan with employer matching
- Life and Disability Insurance
- Employee Assistance Program (EAP)
- Commuter and/or Transit Benefits (if applicable)
*Eligibility for specific benefits may vary based on job classification, schedule (e.g., full\-time vs. part\-time), work location and length of employment.*
Salary Context
This $98K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At WebMD, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($116K) sits 46% below the category median. Disclosed range: $98K to $135K.
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.
WebMD AI Hiring
WebMD has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Newark, NJ, US. Compensation range: $105K - $200K.
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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