Interested in this AI/ML Engineer role at Worldwide Clinical Trials?
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About This Role
Who we are
We’re a global, midsize CRO that pushes boundaries, innovates and invents because the path to a cure for the world’s most persistent diseases is not paved by those who play it safe. It is built by those who take pioneering, creative approaches and implement them with quality and excellence.
We are Worldwide Clinical Trials, and we are a global team of over 3,500+ experts, bright thinkers, dreamers and doers and, together, we are changing the way the world experiences CROs – in the best possible way.
Our mission is to work with passion and purpose every day to improve lives and we are looking for others who value this same pursuit.
Why Worldwide
We believe everyone plays an important role in making a world of difference for patients and their caregivers. From our hands-on, accessible leaders, to our cohesive and supportive teams, we are committed to enabling professionals from all backgrounds and experiences to succeed. We prioritize cultivating a diverse and inclusive environment that continues to promote collaboration and creativity. We are proud to be a workplace where people thrive by being themselves and are inspired to do their best work every day. Join us!
Make an Impact as a Forward Deployed Engineer (Finance Focused) at Worldwide
Help transform how Finance teams work by building AI-powered tools that give analysts valuable time back. In this role, you'll partner closely with finance stakeholders to create practical solutions that accelerate close activities, improve financial analysis, and make financial data easier to access and understand.
You'll work at the intersection of finance, data, and AI, developing tools that assist with variance commentary, financial planning and analysis, and close processes while maintaining strong governance and compliance standards. Your work will help finance professionals focus on higher-value analysis and decision-making.
This is an opportunity for someone who combines finance expertise with software engineering skills and enjoys solving complex business challenges through modern data and AI technologies.
Where You'll Work
This is a REMOTE role open to candidates based in US. You will collaborate with global teams across multiple time zones.
What You'll Do
- Build AI-assisted variance commentary and close-checklist solutions that generate draft insights from financial data and identify key business drivers.
- Develop experiences using Databricks Genie and governed finance data to enable analysts to explore information through natural language.
- Partner with FP&A Analysts and Project Financial Analysts to deliver tools that improve efficiency and enhance financial decision-making.
- Measure and report solution adoption, usage, and productivity gains to demonstrate business impact.
- Create assistive reconciliation capabilities that support finance teams while preserving human ownership of controls and approvals.
- Design and deliver AI-enabled applications using Databricks, Python, and modern LLM technologies.
- Ensure solutions align with governance requirements by maintaining clear separation between AI-generated recommendations and financial control activities.
Collaborate with finance stakeholders to understand processes, close cycles, planning activities, and reporting needs. *
What You'll Bring
- Bachelor's degree in Finance, Accounting, Computer Science, Information Systems, Engineering, or a related field, or equivalent relevant experience.
- 2+ years of hands-on experience with Databricks and building data-driven solutions.
- 2+ years of LLM application engineering experience, including prompt engineering, context engineering, retrieval, structured outputs, and evaluation.
- 2+ years of experience using Python and sufficient full-stack development skills to deliver production-ready applications.
- 2+ years of experience working within or closely alongside Finance teams, with knowledge of FP&A processes, monthly close activities, budgeting, forecasting, variance analysis, and revenue recognition fundamentals.
- Experience working with finance systems and governed financial data, including platforms such as NetSuite, Certinia, and Workday Adaptive Planning.
- Familiarity with Microsoft 365 and Copilot, MLflow, Databricks Vector Search, Databricks Genie, Genie Code, and/or GenieOne; CRO or pharma-services experience and SOX exposure are preferred.
Proficiency in English *
At Worldwide Clinical Trials, we are committed to fostering an inclusive and equitable workplace by providing transparent compensation.
The salary range for this position is annually (For hourly paid positions, the stated salary range reflects hourly rates):
United States of America - $121,500.00 - $240,500.00
The salary range provided is intended to give candidates an understanding of potential earnings. Compensation will fall within the provided range, and it not a guarantee of exact salary. The final salary will be determined based on relevant experience, performance, education, and internal equity. In addition to base salary, we offer a competitive benefits package depending on location. We ensure pay equity and transparency and comply with all applicable labor laws. Salary information will be discussed during the interview process, and we encourage candidates to inquire about compensation at any stage.
We love knowing that someone is going to have a better life because of the work we do.
To view our other roles, check out our careers page at Discover a world of difference at Worldwide ! For more information on Worldwide, visit www.Worldwide.com or connect with us on LinkedIn .
Salary Context
This $121K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Worldwide Clinical Trials, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($181K) sits 15% below the category median. Disclosed range: $121K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Worldwide Clinical Trials AI Hiring
Worldwide Clinical Trials has 2 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer. Positions span Research Triangle Park, NC, US, Trivandrum, Kerala, India. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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