Interested in this AI/ML Engineer role at Radiology Partners?
Apply Now →About This Role
WHO WE ARE AND WHAT WE DO
Mosaic Clinical Technologies™ is pioneering a new imaging paradigm—shifting radiology from reactive diagnostics to proactive, decision-driven care. Through our AI-native platform, MosaicOS™, we unite fragmented technologies into a single, intelligent ecosystem designed to help clinical teams work faster, think smarter and deliver better care.
Built cloud-native and designed for continuous iteration, MosaicOS™ is engineered to solve the industry’s most pressing challenges. Our platform expands clinical capacity, improves diagnostic quality by combining human expertise with AI, and turns operational complexity into strategic advantage through seamless integration or replacement of legacy imaging systems.
Every AI tool within MosaicOS™ is validated across tens of millions of exams to ensure safe, reliable and meaningful clinical impact. With a ransomware-resistant design and built-in redundancy, our platform is designed to ensure continuity of care and protect both patients and operations.
Mosaic Clinical Technologies™ is building technology that evolves with clinicians to advance clinical excellence and improve patient outcomes. If you’re passionate about shaping the future of medical imaging, join us!
POSITION SUMMARY:
- Serve as SME on clinical workflows and AI-enabled tools, supporting end users and guiding stakeholders on adoption and best practices.
- Design, test, and validate clinical and technical workflows to ensure AI solutions are operationally and clinically ready for deployment.
- Build and optimize scalable processes for clinical AI programs, monitoring performance, utilization, and opportunities for improvement.
- Lead cross-functional coordination across engineering, data science, operations, vendors, and clients to ensure alignment and successful delivery.
- Manage end-to-end project execution, including planning, documentation, resource allocation, timelines, and stakeholder communication.
- Translate clinical needs into technical requirements (and vice versa) while driving solution design, integration, and continuous improvement.
- Support AI adoption through user training, documentation, troubleshooting, and ongoing engagement with clinicians, end users, and external clients.
DESIRED PROFESSIONAL SKILLS AND EXPERIENCE:
- 5+ years’ experience supporting radiology and/or related clinical workflows, software & technologies.
- 5+ years’ experience managing clinical and/or technical projects.
- Experience working with FDA clearance is highly preferred
- Bachelor’s degree in a healthcare or technology related field preferred.
- Proven project leader with experience managing complex, multi-project portfolios (AI/IT/Data) across internal teams, vendors, and external clients using Agile and Waterfall methodologies.
- Strong cross-functional communicator who bridges clinical and technical domains, presents effectively to diverse audiences, and delivers training programs that drive AI adoption.
- Highly organized, solutions-oriented self-starter with leadership presence—skilled in prioritization, delegation, risk management, and driving operational efficiency at scale.
WHY MOSAIC CLINICAL TECHNOLOGIES:
- Competitive Benefits package - Eligibility starts the month after hire, with tiered options to choose from.
- Compensation Reviews, Career Growth Opportunities
- Flexible Remote Schedules
- Generous PTO Plans and Paid Holidays
COMPENSATION:
The salary range for this position is $120,000.00 - $140,000.00. Final determinations may vary based on several factors including but not limited to education, work experience, certifications, geographic location etc. This role is also eligible for an annual discretionary bonus. In addition to this range, Radiology Partners offers competitive total rewards packages, which include health & wellness coverage options, 401k benefits, and a broad range of other benefits such as family planning and telehealth (all benefits are subject to eligibility requirements).
Mosaic Clinical Technologies is an equal opportunity employer. MCT is committed to being an inclusive, safe and welcoming environment where everyone has equal access and equitable resources to reach their full potential. We are united by our Mission to Transform Radiology and in turn have an important impact on the patients we serve and the healthcare system overall. We hold that diversity is a key source of strength from which we will build a practice culture that is inclusive for all. Our goal is to empower and engage the voice of every teammate to promote awareness, compassion and a healthy respect for differences.
CCPA Notice: When you submit a job application or resume, you are providing Mosaic Clinical Technologies with the following categories of personal information that the Company will use for the purpose of evaluating your candidacy for employment: (1) Personal Identifiers; and (2) Education and Employment History. Mosaic Clinical Technologies participates in E-verify.
Beware of Fraudulent Messages: Mosaic Clinical Technologies or Radiology Partners will never request payment, banking or other financial information in exchange for interviews or as part of the hiring process. Additionally, we will not send checks for deposit into your bank account at any stage of recruitment. All communication during the interview and hiring process should come from an email address ending in "@radpartners.com." If you suspect you are receiving a fraudulent job offer or solicitation from Radiology Partners or one of our local practices, please email [email protected] to notify our team.
Salary Context
This $120K-$140K range is in the lower quartile 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 Radiology Partners, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $120K to $140K.
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.
Radiology Partners AI Hiring
Radiology Partners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.