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About Parexel
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Parexel is a leading global clinical development partner delivering insights\-driven clinical and consulting solutions to the life sciences industry. With 22,000\+ employees worldwide, we partner with biopharmaceutical companies and research sites to design and deliver patient\-focused clinical trials that broaden access and make research a care option for anyone, anywhere.
We work with focus, agility, and shared purpose \- moving with urgency to address patient needs and accelerate the delivery of new therapies. We embrace bold thinking, adapt quickly to change, and continually raise the bar for the speed and quality of clinical research. We are expanding our team and seeking talented individuals to help drive industry\-leading innovation, With Heart(R).
About the Role
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We are seeking a Senior Director, Light\-Track Delivery \& Automation, to join our AI Enablement team in a remote capacity located on the East Coast. As a Senior Director, you will lead the team that builds and shapes rapid\-turn AI solutions across Parexel including Robotic Process Automations (RPA), AI agents and assistants, and other small\-scale automation \- while enabling business super\-users to build their own solutions. This position plays a key role in delivering strategic AI and automation value quickly and reliably, ensuring governance discipline, and developing high\-performing multidisciplinary teams across geographies.
You'll join a fast\-paced, growth\-oriented environment focused on making a meaningful impact through rapid delivery, scalable automation patterns, and citizen\-developer enablement. With diverse teams and continuous learning opportunities, this role offers the chance to shape the future of AI delivery at a leading clinical research organization and expand your influence across enterprise technology initiatives.
Key Responsibilities
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- Lead the team building rapid\-turn AI solutions across Parexel: RPA automations, AI agents and assistants, and other small\-scale automation work, balancing direct delivery with citizen\-developer enablement
- Manage a portfolio of small\-to\-medium AI and automation initiatives through the enterprise intake process, maintaining visibility on scope, status, value delivered, and available capacity
- Oversee the team that builds AI agents and assistants on platforms including Copilot Studio and Parexel AI Assistant (PAIA), developing reusable patterns and templates that accelerate development
- Lead the RPA team delivering process automations on UiPath and adjacent platforms, managing the intake\-to\-delivery cycle and maintaining the existing RPA portfolio
- Establish and maintain governance discipline that enables fast delivery without sprawl, including triage, platform\-fit decisions, quality standards, and reuse documentation
- Enable and coach business super\-users who build their own agents and assistants, surfacing high\-value patterns for productization
- Serve as senior point of contact for light\-track delivery across all Parexel functions, building trusted relationships with business unit leaders and partnering with cross\-functional teams
- Lead a multidisciplinary team across multiple geographies, including engineers, citizen\-developer enablement specialists, and operations leads, developing talent across both professional engineering and citizen\-developer enablement paths
Candidate Profile
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You'll thrive in this role if you bring:
- Proven delivery leadership across a portfolio of small\-to\-medium AI and automation initiatives
- Excellent prioritization instincts and the ability to make build/skip and platform\-fit decisions confidently
- Strong partnership skills with business stakeholders, technical teams, and citizen developers
- Working knowledge of RPA platforms (UiPath preferred), AI agent/assistant platforms, and enterprise integration patterns
- Experience managing multidisciplinary teams that include both professional engineers and citizen developers
- Strong commercial instincts about which initiatives are worth pursuing and the ability to have honest conversations with business stakeholders
- Comfort working in a regulated environment with familiarity in life sciences or another GxP\-adjacent industry
- Experience working across geographies and managing distributed teams
Required Qualifications
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- Bachelor's degree in Computer Science, Engineering, Business, or related field
- 12\+ years in technology delivery, engineering management, or automation leadership
- At least 5 years leading multidisciplinary teams
- Direct experience leading RPA or process automation programs at enterprise scale
- Experience with citizen\-developer enablement and the operational realities of supporting non\-engineer builders
- Strong understanding of when to build vs. defer, and how to choose among available platforms for a given problem
Preferred Qualifications
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- Advanced degree (Master's or MBA)
- Experience in clinical research, life sciences, or healthcare technology
- Familiarity with regulated environments and compliance requirements
Benefits and Compensation
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- Health, Vision \& Dental Insurance
- Tuition Reimbursement
- Vacation/Holiday/Sick Time
- Flexible Spending \& Health Savings Accounts
- Work/Life Balance
- 401(k) with Company match
- Pet Insurance
For a complete list of benefits, please visit www.parexel.com/us\-benefits.
We offer a comprehensive benefits package to eligible employees, which includes paid time off, a 401(k) match, life insurance, health insurance, and additional benefit offerings in accordance with the terms of our applicable plans.
If this job doesn't sound like the next step in your career, but perhaps you know of someone who'd be a perfect fit, send them the link to apply!
We believe in flexibility, growth, and creating space for people to do their best work. Join us and be part of a team where your contributions help shape the future of clinical research.
\#LI\-REMOTE
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 Parexel, 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. Director-level AI roles across all categories have a median of $274,554.
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.
Parexel AI Hiring
Parexel has 2 open AI roles 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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