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About This Role
- Job ID: JR\-110913
- Entity: Rhode Island Hospital
- Location Name: Rhode Island Hospital
- City, State: Providence, RI
- Work Type: FULL TIME
- Hours Per Week: 40
- Shift: Day
- Posted Date: 6/16/2026
SUMMARY
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- Reports to the Manager Outcomes and Impact.
- Core purpose: Responsible for the strategic development, implementation, and maintenance of pharmacy data science platforms, business information systems, and automation technologies across Brown University Health Pharmacy Services.
- Technology roadmap \& initiatives: Provides advanced systems analysis and data science leadership for projects, applications, and solutions in support of the Pharmacy technology roadmap, enterprise\-wide initiatives, and business\-funded programs.
- Delivery accountability: Directly accountable for effective, high\-quality, and timely analysis and project delivery.
- People leadership support: Supports the Director and Manager, Pharmacy Medication Safety, Quality, and Informatics, in performance management and goal development for team members in alignment with the Brown University Health strategic plan.
- User consultation \& support: Provides expert consultation and ongoing support for information system users across disciplines including Pharmacy, Nursing, and Medical Staff.
- Enterprise integration: Coordinates integration of medication\-related systems with Information Services and enterprise technology partners.
- Compliance \& quality: Ensures Pharmacy systems are developed and maintained in accordance with established policies, procedures, and applicable laws, rules, and regulations; monitors Pharmacy systems through continuous quality improvement activities to ensure stakeholder expectations are consistently met or exceeded.
- Risk \& audit support: Assists in auditing and risk analysis of Pharmacy systems to ensure Brown University Health enterprise requirements are met.
- Policies \& procedures: Participates in the development and implementation of Pharmacy policies and procedures.
- Regulatory readiness: Provides essential Pharmacy data and analytics in support of accreditation and regulatory requirements.
Brown University Health employees are expected to successfully role model the organization's values of Compassion, Accountability, Respect, and Excellence as these values guide our everyday actions with patients, customers, and one another.
Expected to embrace Brown University Health's mission of *Delivering health with care* and successfully role model Brown University Health's values of Compassion, Accountability, Respect, and Excellence as these guide our everyday actions with patients, customers, and one another.
In addition to our values, all employees are expected to demonstrate the core Success Factors which tell us how we work together and how we get things done. The core Success Factors include:
- Instill Trust and Value Differences
- Patient and Community Focus and Collaborate
PRINCIPAL DUTIES AND RESPONSIBILITIES
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- Brown University Health employees are expected to successfully role model the organization's values of Compassion, Accountability, Respect, and Excellence as these guide our everyday actions with patients, customers, and one another.
- Implements, interprets, and provides expert consultation on policies, procedures, and standards of practice for the effective use, maintenance, and governance of Pharmacy information systems, data platforms, and automation technologies.
- Continuously reviews and evaluates systems architecture, recommending and implementing software, hardware, and platform upgrades; evaluates and responsibly adopts emerging technologies that enhance pharmacy efficiency, patient safety, and service delivery.
- Leads the design, development, and ongoing governance of the Pharmacy's data science program, including statistical learning models, predictive analytics, and artificial intelligence tools to support clinical and operational decision\-making.
- Ensures the ethical, responsible, and compliant application of machine learning (ML) and artificial intelligence (AI) tools in pharmacy operations, adhering to organizational and regulatory frameworks for AI governance and patient safety.
- Develops and maintains programs, information systems, and automation workflows to enhance medication safety, streamline operations, track clinical activities, and improve inter\- and intra\-departmental communication.
- Extracts, transforms, and integrates data from diverse file formats and information systems to develop consolidated, actionable reports and dashboards supporting operational and clinical intelligence needs.
- Develops and maintains interactive, web\-based databases and data visualization tools for monitoring, querying, reporting, and managing pharmacy data and project deliverables.
- Programs, monitors, and manages automated workflows and reporting tools; creates data integrity monitoring tools and takes appropriate corrective action as warranted.
- Formulates and defines system scope and project objectives based on stakeholder\-defined needs; devises or modifies procedures to solve complex problems with consideration of system capacity, constraints, operating requirements, and desired outcomes.
- Coordinates the integration of Pharmacy information systems and technologies with enterprise platforms, including EHR/CPOE systems, automated dispensing systems, and cloud\-based solutions, as well as with other affiliates and external agencies.
- Provides input and assists in the development of effective budgeting and allocation of resources including people, financial, material, and equipment to achieve agreed\-upon goals and objectives. Assists in development of systems and databases to support ongoing analysis of salary and non\-salary expense and revenue allocation.
- Participates in the development of short\- and long\-term Pharmacy goals in alignment with Brown University Health's mission, values, and key business objectives; identifies and recommends resources required to achieve operational results.
- Leads and facilitates weekly team meetings to review, prioritize, and manage projects and operational objectives; fosters a collaborative team environment in which members are empowered to do their best work.
- Provides functional guidance, input, and direction to Business Operations and Automation personnel in collaboration with the Manager, Pharmacy Medication Safety, Quality, and Informatics.
- Assists the Manager, Pharmacy Medication Safety, Quality, and Informatics in yearly performance appraisals for team members, providing structured feedback on work performance and input on future professional development objectives.
- Coordinates with Pharmacy business partners to identify and specify complex business requirements and processes; participates in application design, testing, and implementation; clearly documents and communicates system changes to stakeholders while maintaining strong ongoing relationships with business and systems partners.
- Develops and maintains supporting tools and data infrastructure for the 340B Drug Discount Program, providing reporting and data necessary to ensure program compliance; assists 340B Coordinators with self\-monitoring analysis.
- Provides Pharmacy data, analytics, and reports necessary to meet the requirements of accreditation and regulatory bodies including The Joint Commission (TJC), URAC, and the Rhode Island Board of Pharmacy.
- Applies systems analysis techniques and procedures, including consulting with users to determine hardware, software, or system functional specifications.
- Participates in continuous quality improvement (CQI) activities and total quality management (TQM) initiatives; participates in or leads committees, task forces, and performance improvement teams as assigned.
- Assists pharmacy personnel in the preparation of posters, abstracts, and manuscripts for presentation at professional meetings and for publication in peer\-reviewed journals; assists Pharmacy Business Operations and Automation Specialist personnel in maintenance of the Pharmacy web presence.
- Maintains current expertise in leading\-edge developments in pharmacy informatics, data science, automation, and responsible AI through ongoing professional development, independent study, and professional affiliations.
- Supports all hospital and retail Pharmacy departments across Brown University Health with pharmacy systems integration and data extraction needs.
- Participates in the on\-call rotating schedule for the Pharmacy Technology and Systems group.
- Occasional travel between facilities or sites is required.
- Performs other duties as assigned.
MINIMUM QUALIFICATIONS
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### BASIC KNOWLEDGE:
Required
- Bachelor's degree in Computer Science, Data Science, Health Informatics, Biomedical Informatics, Business Administration, or a related field.
Preferred
- Master's degree in Data Science, Health Informatics, Biomedical Informatics, or a related advanced discipline.
Knowledge and familiarity
- Working knowledge of pharmacy information systems, automated dispensing technologies, database architecture, and enterprise data platforms.
- Familiarity with healthcare regulatory and accreditation standards including The Joint Commission (TJC), URAC, and the Rhode Island Board of Pharmacy.
- Understanding of responsible AI/ML governance frameworks and applicable healthcare data privacy regulations (e.g., HIPAA).
- Familiarity with cloud\-based data infrastructure platforms (e.g., Microsoft Azure, AWS, Google Cloud, Snowflake) is increasingly expected for roles of this level and is recommended as a preferred qualification.
### EXPERIENCE:
- Minimum of five (5\) years of progressively responsible, related experience, preferably gained in a similarly diverse and operationally complex health care environment.
- Demonstrated expertise in database architecture and querying languages (e.g., SQL, NoSQL).
- Proficiency in advanced programming languages (e.g., Python, R) and scripting or workflow automation tools.
- Experience with statistical learning models and machine learning methodologies, including design, training, validation, and implementation (e.g., logistic regression, multi\-level modeling, neural networks).
- Experience with large language model (LLM) integration or generative AI applications in clinical or operational pharmacy settings is an emerging and recommended preferred qualification for this role level.
- Demonstrated experience designing ethical and responsible machine learning and artificial intelligence solutions in a healthcare or regulated environment.
- Experience with data visualization and business intelligence platforms (e.g., Tableau, Power BI, or equivalent) preferred.
- Project management experience including facilitation, prioritization, and resource management. PMP or equivalent certification preferred.
- Experience with 340B Drug Discount Program data systems and compliance reporting preferred.
- Effective verbal and written communication skills.
INDEPENDENT ACTION:
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- Functions independently within a broad scope of departmental policies and practices.
- Exercises independent judgment in the design, analysis, and implementation of pharmacy data systems and automation solutions.
- Refers specific complex problems or novel situations requiring policy interpretation to the Manager or Director, Pharmacy Medication Safety, Quality, and Informatics, where clarification of departmental policies and procedures may be required.
SUPERVISORY RESPONSIBILITY:
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- None
Pay Range
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$86,382\.40\-$142,542\.40
Location
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Rhode Island Hospital \- 593 Eddy Street Providence, Rhode Island 02903
Work Type
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M\-F 8:00am\-4:30pm
Work Shift
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Day
Daily Hours
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8 hours
Driving Required
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No
*Brown University Health is committed to providing equal employment opportunities and maintaining a work environment free from all forms of unlawful discrimination and harassment.*
Salary Context
This $86K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Brown University Health, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($114K) sits 38% below the category median. Disclosed range: $86K to $142K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Brown University Health AI Hiring
Brown University Health has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Based in Providence, RI, US. Compensation range: $142K - $340K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.
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