Interested in this AI/ML Engineer role at American Diabetes Association?
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About This Role
The American Diabetes Association (ADA) is seeking a Manager, Medical Affairs to support the development and execution of high\-impact scientific and clinical initiatives. This role is ideal for candidates interested in working at the intersection of scientific strategy, evidence\-based medicine, and clinical guideline development.
The Manager will play a key role in coordinating scientific projects, supporting stakeholder engagement, and ensuring that scientific content is accurate, high quality, and aligned with ADA Standards of Care and clinical guidance. This position offers the opportunity to collaborate with leading experts, contribute to nationally recognized initiatives, and support programs that shape diabetes care.
This individual will work cross\-functionally with ADA teams, external stakeholders, and health care professionals in a dynamic, fast\-paced environment.
RESPONSIBILITIES
- Collaborate with internal teams, sponsors, and key opinion leaders to support the planning and execution of Medical Affairs programs and scientific initiatives.
- Assist in the review, formatting, and quality control of scientific content, ensuring alignment with ADA Standards of Care, clinical guidance, and evidence\-based practices.
- Support development and dissemination of scientific materials, reports, and stakeholder communications, ensuring clarity, accuracy, and consistency.
- Communicate project plans, timelines, and milestones clearly to drive alignment and execution across stakeholders.
- Organize and support virtual and in\-person scientific meetings, including agenda development, material preparation, stakeholder coordination, and meeting execution.
- Maintain detailed project documentation, including meeting minutes, action items, and scientific records.
- Manage follow\-up activities, including tracking deliverables, coordinating expense reporting, and processing honoraria in collaboration with ADA teams.
- Represent ADA in stakeholder engagements with professionalism, preparation, and strong understanding of scientific topics.
- Support distribution of materials via platforms such as SharePoint, Basecamp, and Smartsheet.
- Maintain and organize scientific documentation and stakeholder records (e.g., disclosures, authorship forms, agreements).
- Contribute to the organization and maintenance of scientific libraries, including document formatting, proofreading, and version control.
QUALIFICATIONS
- Bachelor’s degree required; degree in health sciences, public health, or a related field preferred.
- Experience or demonstrated interest in scientific content review, evidence\-based medicine, or clinical guideline development is highly desirable.
- Excellent written and verbal communication skills, including ability to support scientific documentation and stakeholder communication.
- Strong organizational and project management skills, with the ability to manage multiple projects and competing deadlines.
- Proficiency in Microsoft Office (Word, Excel, PowerPoint) and familiarity with collaboration tools (e.g., SharePoint, Basecamp, Smartsheet) preferred.
- Ability to work both independently and collaboratively in a team\-oriented, fast\-paced environment.
- Strong attention to detail with a focus on accuracy, quality, and consistency in scientific materials.
- Demonstrated problem\-solving skills and ability to adapt to evolving project needs.
WHY WORK HERE
The American Diabetes Association (ADA) offers a rewarding career working for one of the premier voluntary health organizations in the world supporting people with type 1, type 2 and gestational diabetes. Our employees like working at the ADA because of our mission, the inclusive environment, work\-life balance, our benefits and our culture:
- Industry competitive base pay ranging from $60,000 \- $68,000 for this role. Base offers are determined by several factors including but not limited to your relevant work experience, education, certifications, location, internal pay equity, etc.
- A culture of recognition including new hire welcome announcements, service anniversary awards, referral bonuses, monthly All Employee Assembly, appreciation awards
- Generous Paid Time Off, including holidays, vacation days, personal days and sick days
- Comprehensive benefits package including medical, dental, vision, Flexible Spending Accounts (FSA), disability \& life insurance, pet insurance and retirement savings
- Guided by our mission, we provide top tier diabetes supply coverage through our medical benefits program
- A company focus on offering mental health programs and work/life balance with most of our employees working remote
- Joining our dedicated team affords the gratification of knowing beyond a doubt that you will impact the lives and well\-being of millions
Salary Context
This $60K-$68K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At American Diabetes Association, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($64K) sits 62% below the category median. Disclosed range: $60K to $68K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
American Diabetes Association AI Hiring
American Diabetes Association has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $68K - $90K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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