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About This Role
Company Description
At EVERSANA®, we are proud to be a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 6,000 employees is committed to creating and delivering next\-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 670 clients ranging from innovative biotech start\-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!
Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs. We are deliberate and self\-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA.
Job Description
The EVERSANA Internship Program is a 10\-week paid position designed to offer college graduates the opportunity to learn about a career in life sciences. Interns will work in real\-world business situations, be paired with a mentor, or team, to discover different aspects of the industry, and learn about the inner workings of our practice.
As an Innovation Department Intern, you will support the team in identifying, researching, and developing new ideas and technologies that drive the company’s growth and competitive edge. This role offers a unique opportunity to gain hands\-on experience in a dynamic and forward\-thinking environment, where creativity and problem\-solving skills are highly valued.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:
- Develop and fine\-tune LLM\-powered applications for internal teams and pharmaceutical clients.
- Build AI\-powered agents to assist in areas like drug commercialization, market intelligence, competitive analysis, and procurement.
- Design and implement RAG\-based (Retrieval\-Augmented Generation) solutions using enterprise CRM and knowledge data.
- Collaborate with software engineers and domain experts to deploy AI models into production.
- Work with APIs (e.g., OpenAI, Anthropic, Google Gemini, Cohere, Hugging Face) to integrate generative AI capabilities into applications.
- Experiment with multimodal AI models for text, image, and audio processing in pharmaceutical use cases.
- Optimize AI models for efficiency, scalability, and compliance with pharmaceutical industry regulations.
- Contribute to new AI\-driven products and prototypes for the pharma industry.
- Demonstrate a commitment to diversity, equity, and inclusion through continuous development, modeling inclusive behaviors, and proactively managing bias.
- All other duties as assigned.
EXPECTATIONS OF THE JOB:
- Travel (0% or number of days)
- Hours (40 Hours per week, Monday \- Friday)
Qualifications MINIMUM KNOWLEDGE, SKILLS AND ABILITIES:
The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.
Education
- Bachelor’s degree in Engineering, Math, Physics, Computer Science, or related area of study
Experience and/or Training
- Interest in the healthcare and/or pharmaceutical industry
- Prior experience with artificial intelligence (AI) technologies and applications a plus.
- Has knowledge of commonly used concepts, practices, and procedures within project management
- Demonstrated proactivity, self\-awareness and accountability
- Strong analytical skills, problem\-solving ability, conceptual thinking, \& communication skills
- Experience working in a collaborative environment with cross\-functional teams.
- Time management and multi\-tasking abilities in fast\-paced remote working environment
- Strong leadership ability
- Ability to develop creative, well\-structured PowerPoint slides or presentations
- Appetite for learning and ability to work in an independent remote environment
- Eagerness to learn new technologies and skills.
- Ability to prioritize and be comfortable pivoting as needed in a fast\-paced environment.
Technology/Equipment Excel, Word, PowerPoint, Outlook
Additional Information OUR CULTURAL BELIEFS:
Patient Minded I act with the patient’s best interest in mind.
Client Delight I own every client experience and its impact on results.
Take Action I am empowered and empower others to act now.
Grow Talent I own my development and invest in the development of others.
Win Together I passionately connect with anyone, anywhere, anytime to achieve results.
Communication Matters I speak up to create transparent, thoughtful and timely dialogue.
Embrace Diversity I create an environment of awareness and respect.
Always Innovate I am bold and creative in everything I do.
Our team is aware of recent fraudulent job offers in the market, misrepresenting EVERSANA. Recruitment fraud is a sophisticated scam commonly perpetrated through online services using fake websites, unsolicited e\-mails, or even text messages claiming to be a legitimate company. Some of these scams request personal information and even payment for training or job application fees. Please know EVERSANA would never require personal information nor payment of any kind during the employment process. We respect the personal rights of all candidates looking to explore careers at EVERSANA.
EVERSANA is committed to providing competitive salaries and benefits for all employees. If this job posting includes a base salary range, it represents the low and high end of the salary range for this position and is not applicable to locations outside of the U.S. Compensation will be determined based on relevant experience, other job\-related qualifications/skills, and geographic location (to account for comparative cost of living). More information about EVERSANA’s benefits package can be found at eversana.com/careers. EVERSANA reserves the right to modify this base salary range and benefits at any time.
From EVERSANA’s inception, Diversity, Equity \& Inclusion have always been key to our success. We are an Equal Opportunity Employer, and our employees are people with different strengths, experiences, and backgrounds who share a passion for improving the lives of patients and leading innovation within the healthcare industry. Diversity not only includes race and gender identity, but also age, disability status, veteran status, sexual orientation, religion, and many other parts of one’s identity. All of our employees’ points of view are key to our success, and inclusion is everyone's responsibility.
Consistent with the Americans with Disabilities Act (ADA) and applicable state and local laws, it is the policy of EVERSANA to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for EVERSANA. The policy regarding requests for reasonable accommodations applies to all aspects of the hiring process. If reasonable accommodation is needed to participate in the interview and hiring process, please contact us at [email protected].
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EVERSANA, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EVERSANA AI Hiring
EVERSANA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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