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About This Role
### Description and Requirements
ABOUT WCG:
WCG’s clinical solutions are built on a foundation of best\-in\-class clinical services companies. We deliver transformational solutions that stimulate growth, foster compliance, and maximize efficiency for those performing clinical trials. WCG is proud to serve individuals on the frontlines of science and medicine, and the organizations striving to develop new products and therapies to improve the quality of human health. It is our role to empower them to accelerate advancement, while ensuring the risks of progress never outweigh the value of human life.
WHY WE LOVE WCG:
At WCG, our employees are our most valuable asset and as with all our assets, we invest in them with an eye toward future success. We provide each eligible employee with a comprehensive set of benefits designed to protect their personal and financial health and to help them make the most of their future.
- Comprehensive Benefits package \- Health, Dental, Vision, Life Disability, 401k with match, and flexible spending accounts
- Employee Assistance Programs and additional work/life resources
- Referral Bonuses and Tuition Reimbursement
- Paid time off including holidays, vacation, and sick time
- Opportunities for career development with on\-the\-job training, certification assistance and continuing education reimbursement
The expected base salary range for this position is $151,200 to $275,000\. This salary range may vary based on the candidate's qualifications, experience, skills, education, and geographic location.JOB SUMMARY:
The Sr. Director, AI Engineering is responsible for leading the design, development, deployment, and scaling of enterprise AI solutions across the organization. This role drives the implementation and operationalization of AI platforms, including machine learning, generative AI, and intelligent automation capabilities.
This role balances strategic direction with hands\-on execution oversight, ensuring AI solutions deliver measurable business value while meeting regulatory, quality, and operational standards.
ESSENTIAL DUTIES/RESPONSIBILITIES: To perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The accountabilities listed below are representative of the knowledge, skills, and/or ability required.
- Execute and operationalize the enterprise AI strategy, ensuring alignment to business priorities and measurable outcomes
- Drive design, adoption and growth of AI platforms (ML, GenAI, MLOps, LLMOps) across clinical, regulatory, and operational domains
- Lead prioritization of AI initiatives, balancing short\-term delivery and long\-term platform evolution
- Partner with executive stakeholders to translate business needs into scalable AI solutions
- Establish and scale enterprise\-wide architectures for AI model development, deployment, monitoring, and lifecycle management.
- Drive implementation and continuous improvement of AI platform infrastructure and pipelines
- Ensure AI platforms integrate seamlessly with enterprise data and technology ecosystems
- Establish and enforce Responsible AI frameworks including model validation, bias mitigation, explainability, and auditability
- Establish AI governance aligned with regulatory frameworks (GxP, ALCOA\+, data integrity)
- Build, lead, and scale high\-performing, globally distributed AI engineering teams
- Drive workforce planning, resource allocation, and capability building in AI engineering
- Collaborate cross\-functionally with product, data, compliance, and IT teams.
- Manage strategic vendor relationships and AI\-related third\-party platforms.
- Other duties as assigned by supervisor. These may, on occasion, be unrelated to the position described here.
EDUCATION REQUIREMENTS:
- Bachelor’s degree in Computer Science or related field required; Master’s preferred.
CERTIFICATIONS/LICENSE/REGISTRATION REQUIREMENTS:
- None
QUALIFICATIONS/EXPERIENCE:
- 10\+ years of experience in AI/ML, data engineering, or advanced analytics.
- 7\+ years of leadership experience managing global engineering or AI teams.
- Experience building and scaling AI and ML platforms in production environments.
- Demonstrated ability to lead large\-scale, cross\-functional initiatives with measurable outcomes
- Deep knowledge of ML, NLP, and Generative AI technologies.
- Experience using cloud platforms such as Azure, AWS, or GCP.
- Experience managing vendors and distributed/offshore teams
- Familiarity with MLOps and LLMOps practices.
- Experience working in regulated environments (strongly preferred).
SUPERVISORY RESPONSIBILITIES: Overall responsibility of management including direction, coordination, performance, and evaluation of the assigned team and staff. Responsibilities include training employees; planning, assigning, and directing work; appraising performance; rewarding and disciplining employees; addressing complaints and resolving problems.
TRAVEL REQUIREMENTS: 10% to 20%
WCG is proud to be an equal opportunity employer – Qualified applicants will receive consideration for employment based on merit and without regard to race, color, national origin or ancestry, religion or creed, sex, sexual orientation, gender expression, gender identity, age, marital status, family or parental status, disability, genetic information, citizenship, veteran status, or any other legally recognized basis or status protected by federal, state, or local law. WCG complies with the Vietnam Era Veterans' Readjustment Act and Section 503 of the Rehabilitation Act. We promote a "One WCG" culture where all are welcome, respected, valued, and empowered to make a difference every day to advance clinical research.
Salary Context
This $151K-$275K range is above the median 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 WCG Clinical, 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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $151K to $275K.
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.
WCG Clinical AI Hiring
WCG Clinical has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $171K - $275K.
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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