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About This Role
Posting Date
10/02/2026 2000 16th Street, Denver, Colorado, 80202, United States of America1. Strategic Leadership & Vision (30%)
- Develop and execute a strategic roadmap for AI/ML development that aligns with DaVita's clinical and business objectives.
- Expand the enterprise-wide data science capabilities by focusing on complex deep learning techniques, Generative AI (GenAI), and natural language processing (NLP).
- Champion the ROI of predictive modeling and identify marquee opportunities, presenting findings and strategic recommendations to senior Technology and Business leaders.
2. Team Leadership & Development (25%)
- Lead, mentor, and grow a high-performing data science team (directing multiple direct and indirect reports).
- Instill uncompromising methodological rigor; ensure models undergo rigorous feature engineering, calibration, and threshold selection before transitioning from POC to production.
- Foster a culture of quality, innovation, and agile delivery rooted in the "DaVita Way" and Core Values (CaSP).
3. Model Development & Delivery (25%)
- Oversee the end-to-end delivery of major modeling initiatives (e.g., Hospital Discharge Summary solutions, 90-day PCT retention models, CMS Survey predictions, and LLM fine-tuning).
- Direct the operationalization of models via centralized ML Pipelines and the Vertex AI Feature Store (VFS) to accelerate data science velocity and reduce deployment friction.
- Drive the scaling of custom-built model monitoring services (e.g., VigiLens, Arize) to ensure sustained performance, fairness, and accuracy of enterprise AI products.
4. Cross-Functional Collaboration & Governance (20%)
- Partner seamlessly with AI Engineering, IT Architecture, Medical Informatics, and Clinical/Business stakeholders to weave AI/ML into integrated delivery roadmaps.
- Spearhead the adoption of Responsible AI (RAI) and Explainable AI (xAI) frameworks, ensuring all models adhere to fairness, privacy, security, and healthcare compliance standards.
- Collaborate with the broader Technology People Services and Data Intelligence organizations to cross-train talent and build DaVita’s internal AI bench.
Total Allocation of Time: 100%
MINIMUM QUALIFICATIONS
Education:
- Master’s degree or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field is Required.
Experience:
- 7-10+ years of applied experience in data science, advanced analytics, or machine learning with proven results.
- 3-5+ years of leadership or management experience, successfully building and scaling data science teams.
- Experience within the healthcare industry (handling clinical data, EHR, or healthcare interoperability standards like HL7/FHIR) is highly Preferred.
Technical Skills:
- Deep expertise in machine learning, deep learning, NLP, and Generative AI (e.g., LLMs, transformers, GANs).
- Advanced proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Extensive hands-on experience with the Google Cloud Platform (GCP) software stack, including Vertex AI, BigQuery, AI Platform, and Dataflow.
- Strong understanding of MLOps best practices, centralized ML pipelines, and model monitoring/observability tools (e.g., Arize).
Soft Skills:
- Masterful ability to translate complex statistical blockers into actionable business decisions for non-technical leadership.
- Exceptional strategic scaling capabilities-balancing continuous model maturity with reusable tools and services.
- Refuses to shortcut the data science process, showing a track record of actively pushing back on aggressive timelines to ensure safety and rigor in production.
What We’ll Provide:
More than just pay, our DaVita Rewards package connects teammates to what matters most. Teammates are eligible to begin receiving benefits on the first day of the month following or coinciding with one month of continuous employment. Below are some of our benefit offerings.
- Comprehensive benefits: Medical, dental, vision, 401(k) match, paid time off, PTO cash out
- Support for you and your family: Family resources, EAP counseling sessions, access Headspace®, backup child and elder care, maternity/paternity leave and more
- Professional development programs: DaVita offers a variety of programs to help strong performers grow within their career and also offers on-demand virtual leadership and development courses through DaVita’s online training platform StarLearning.
At DaVita, we strive to be a community first and a company second. We want all teammates to experience DaVita as "a place where I belong." Our goal is to embed belonging into everything we do in our Village, so that it becomes part of who we are. We are proud to be an equal opportunity workplace and comply with state and federal affirmative action requirements. Individuals are recruited, hired, assigned and promoted without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or any other protected characteristic.
This position will be open for a minimum of three days.
The Salary Range for the role is $129,000.00 - $205,000.00/year. For location-specific minimum wage details, see the following link: DaVita.jobs/WageRates
Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies and experience. DaVita offers a competitive total rewards package, which includes a 401k match, healthcare coverage and a broad range of other benefits. Learn more at https://careers.davita.com/benefits
Colorado Residents: Please do not respond to any questions in this initial application that may seek age-identifying information such as age, date of birth, or dates of school attendance or graduation. You may also redact this information from any materials you submit during the application process. You will not be penalized for redacting or removing this information.
Beware of Recruitment Fraud
DaVita will never ask for payment or personal financial information at any point in the hiring process, nor will we ever communicate with you using email addresses outside of the DaVita secure network. If you receive a request like this, it is not legitimate. Do not share your information.
Career Growth - From clinical and operations to corporate jobs, our learning and development programs help guide your career journey.
Salary Context
This $129K-$205K range is below 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 DaVita, 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $129K to $205K.
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.
DaVita AI Hiring
DaVita has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $205K - $205K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national 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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