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About This Role
Job Summary:
Candidates with all levels of degrees in a technical background \- join us onsite on Thursday, August 20th to learn about a variety of open positions within various departments here in Cincinnati.
Medpace will be hosting an In\-Person Hiring Event regarding our opportunities for candidates with technical, analytical, and/or data focused backgrounds in our Cincinnati, OH office. WHEN: Thursday, August 20th at 5:30 PM EST
If interested, please fill out an application below. More details regarding the format of the event will be provided should you be selected to attend. We're excited to speak to you!
Responsibilities :
Below are the positions that will be represented at the event: Clinical Informatics Analyst \- Insights \& Analytics* Evaluate data needs for assigned projects and make recommendations on strategic approach specific to the study design;
- Work alongside operational and medical leads to contribute innovative data products;
- Interact with relational data at both study\- and enterprise\-level inclusive of actions for query, wrangling, analysis, and reporting;
- Innovate algorithms to describe, classify, and predict operational outcomes of clinical trials; and
- Support departmental process improvement initiatives.
Feasibility Informatics Analyst \- Feasibility \& Proposals* Evaluate data needs for assigned projects and make recommendations on strategic approach specific to the study design and opportunity specifics;
- Perform comprehensive review of data sources to deliver high quality informatics data and analysis to teams;
- Work alongside global feasibility leads to contribute to proposal strategy for site and country selection based on available data;
- Translate the results of feasibility research and analysis into compelling data visualizations which illustrate the overall feasibility strategy including enrollment modeling;
- Design and implement database architecture plans and perform custom queries for methodological and clinical data sources;
- Perform development and review of proposal text;
- Assist project teams with preparation for bid defense meetings; and
- Support departmental process improvement initiatives.
Data Engineer \- IT* Utilize skills in development areas including data warehousing, business intelligence, and databases (Snowflake, ANSI SQL, SQL Server, T\-SQL);
- Support programming/software development using Extract, Transform, and Load (ETL) and Extract, Load and Transform (ELT) tools, (dbt, Azure Data Factory, SSIS);
- Design, develop, enhance and support business intelligence systems primarily using Microsoft Power BI;
- Collect, analyze and document user requirements;
- Participate in software validation process through development, review, and/or execution of test plan/cases/scripts;
- Create software applications by following software development lifecycle process, which includes requirements gathering, design, development, testing, release, and maintenance;
- Communicate with team members regarding projects, development, tools, and procedures; and
- Provide end\-user support including setup, installation, and maintenance for applications
Data Engineer \- AI* Utilize skills in handling of more unconventional data such as unstructured content from web\-based sites and varying content (documents, images etc) into different data lakes and with different software solutions (Snowflake, Azure, SQL, Python);
- Provide the handling of (and where needed training of) Large Language Models (LLMs) in the Extract, Transform, and Load (ETL) of large corpus of data into a data lake;
- Participate in the Natural Language Processing (NLP) extract of unstructured data into structured meta\-data through the use of tools such as Semantic understanding and meaning (Python, use of REST API);
- Support ensuring the data flow of any external content coming in is handled to the latest US and EU AI Acts concerning AI which includes security, confidentiality and privacy of PHI;
- Collect, analyze and document user requirements working with AI engineers to align data sources to downstream integration within systems;
- Create software applications that support the understanding and visualization of data flows from inception to derivation whilst maintaining version control by following software development lifecycle process, which includes requirements gathering, design, development, testing, release, and maintenance;
- Participate in software validation process through development, review, and/or execution of test plan/cases/scripts;
- Communicate with team members regarding projects, development, tools, and procedures; and
- Provide end\-user support including setup, installation, and maintenance for application
Business Intelligence Analyst \- Data Engineering IT* Technical Skills – either experience in, or strong desire to learn fundamental technical skills needed to drive Data initiatives (SQL, Python, Dimensional Modeling, etc.);
- Communication Skills \- Partner with other data engineers to implement data architecture and design, to support complex analysis;
- Analytical Skills \- Conduct complex analysis and proactively identify key business insights to assist departmental decision making.
- Data Visualization skills \- Designing and developing key metrics, reports, and dashboards to drive insights and business decisions to improve performance and reduce costs;
Qualifications :
- BS, MS or PhD in Computer Science/Engineering, Data Science, Informatics, Data Analytics, Statistics, or related field(s)
- 3\.5 GPA or higher
- Willing to relocate or currently located in Cincinnati, OH
Medpace Overview :
Medpace is a full\-service clinical contract research organization (CRO). We provide Phase I\-IV clinical development services to the biotechnology, pharmaceutical and medical device industries. Our mission is to accelerate the global development of safe and effective medical therapeutics through its scientific and disciplined approach. We leverage local regulatory and therapeutic expertise across all major areas including oncology, cardiology, metabolic disease, endocrinology, central nervous system, anti\-viral and anti\-infective. Headquartered in Cincinnati, Ohio, employing more than 5,000 people across 40\+ countries.
Why Medpace?:
People. Purpose. Passion. Make a Difference Tomorrow. Join Us Today.
The work we’ve done over the past 30\+ years has positively impacted the lives of countless patients and families who face hundreds of diseases across all key therapeutic areas. The work we do today will improve the lives of people living with illness and disease in the future. Cincinnati Perks* Cincinnati Campus Overview
- Flexible work environment
- Competitive PTO packages, starting at 20\+ days
- Competitive compensation and benefits package
- Company\-sponsored employee appreciation events
- Employee health and wellness initiatives
- Community involvement with local nonprofit organizations
- Discounts on local sports games, fitness gyms and attractions
- Modern, ecofriendly campus with an on\-site fitness center
- Structured career paths with opportunities for professional growth
- Discounted tuition for UC online programs
Awards* Named a Top Workplace in 2024 by The Cincinnati Enquirer
- Recognized by Forbes as one of America's Most Successful Midsize Companies in 2021, 2022, 2023 and 2024
- Continually recognized with CRO Leadership Awards from Life Science Leader magazine based on expertise, quality, capabilities, reliability, and compatibility
What to Expect Next
A Medpace team member will review your qualifications and, if interested, you will be contacted with details for next steps.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Medpace, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Medpace AI Hiring
Medpace has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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