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About This Role
Join the Team Modernizing Medicine
At ModMed, we’re not just building software—we’re reimagining the healthcare experience. Founded in 2010 by a practicing physician and a successful tech entrepreneur, we took a radically different approach: we hired doctors and taught them how to code. This "for doctors, by doctors" philosophy has allowed us to create an AI-enabled, specialty-specific cloud platform that places patients at the center of care.
A Culture of Excellence
When you join ModMed, you’re joining an award-winning team recognized for innovation and employee satisfaction. From our global headquarters in Boca Raton Florida, and extensive employee base in Hyderabad India, we are a team of 4,500+ passionate problem-solvers on a mission to increase medical practice success and improve patient outcomes:
- Consistently ranked as a Top Place to Work
- 2025 Globee Business Awards: Gold Globee for “Technology Team of the Year”
- 2025 Black Book Awards: Ranked #1 EHR in 11 Specialties
- Florida Venture Forum: Venture-Backed Company of the Year
We are growing fast, thinking big, and we are just getting started.
Ready to modernize medicine with us?
Job Description Summary:
The Staff Engineer - Enterprise AI Platforms & Infrastructure is the dedicated technical owner for Claude Enterprise and ModMed's enterprise AI infrastructure, responsible for transitioning these capabilities from distributed, part-time ownership into a scalable enterprise platform operating model. The role initially leads the technical deployment and operation of Claude Enterprise and Claude Code, establishing the identity, security, endpoint, policy, integration, monitoring, and operational foundations required for enterprise adoption. The role then expands those capabilities into a broader enterprise AI platform that securely connects AI assistants and agents to ModMed applications, APIs, data platforms,
and business systems through reusable infrastructure and governed services.
Your Role:
Claude Enterprise Platform Engineering & Operations: Owns the technical architecture, deployment, migration, configuration, and ongoing operation of Claude Enterprise and Claude Code across ModMed. Responsibilities include enterprise identity and SSO integration, domain configuration, user provisioning, role and permission design, endpoint configuration, enterprise policy enforcement, usage/spending controls, security configurations, deployment standards, and operational support. Establishes the migration path from individually managed or unmanaged Claude/Claude Code usage into the governed enterprise environment.
Enterprise AI Infrastructure & Platform Engineering: Designs, builds, and operates the shared infrastructure required to support enterprise AI platforms, assistants, agents, and AI-enabled applications. Establishes reusable capabilities for authentication, authorization, secrets management, networking, API access, service discovery, policy enforcement, logging, deployment, and runtime operations. Builds infrastructure-as-code, deployment pipelines, configuration standards, runbooks, and reference architectures.
MCP Gateway, AI Gateway & Enterprise Connectivity: Builds and operates ModMed's enterprise MCP and AI connectivity infrastructure, including the MCP gateway and related services. Federates enterprise MCP servers and connectors behind governed infrastructure and establishes repeatable patterns for connecting AI platforms to enterprise applications, APIs, data platforms, and services. Owns platform deployment, networking, TLS/certificates, authentication flows, scaling, availability, upgrades, and disaster recovery.
AI Identity, Access & Platform Security Engineering: Engineers the identity and access architecture supporting enterprise AI platforms and agents. Integrates AI infrastructure with enterprise IAM, including Okta, OAuth/OIDC, scoped tokens, service identities, user-to-agent attribution, secrets management, and least-privilege access. Partners with Security, Privacy, and Compliance to translate policy requirements into enforceable technical platform controls.
Platform Observability, Reliability & Operations: Establishes enterprise monitoring, logging, tracing, alerting, usage telemetry, capacity management, and operational health standards across AI infrastructure. Defines service-level expectations and monitors platform availability, latency, errors, utilization, authentication failures, connector health, and consumption. Leads technical incident response, root-cause analysis, lifecycle management, upgrades, and operational improvement.
AI Platform Architecture & Technical Roadmap: Defines and maintains ModMed's enterprise AI platform and infrastructure architecture and technical roadmap. Evaluates emerging AI infrastructure technologies and determines how capabilities such as Claude Enterprise, AWS Bedrock, MCP, AI gateways, Databricks AI capabilities, cloud services, and enterprise application integrations fit into the target architecture. Creates reference architectures and platform standards that enable teams to consume AI capabilities without rebuilding foundational services.
Skills & Requirements:
- Bachelor's Degree in Computer Science, Information Systems, Cyber Security, AI, Applied AI or related.
- 10+ years in platform/infrastructure or security engineering, with demonstrated deep hands-on delivery at a IAM and OAuth/OIDC; API gateways; cloud (GCP and/or AWS); infrastructure-as-code; observability/monitoring; security and compliance frameworks (HIPAA, SOC 2); scripting/automation.
ModMed Benefits Highlight: At ModMed, we believe it’s important to offer a competitive benefits package designed to meet the diverse needs of our growing workforce. Eligible Modernizers can enroll in a wide range of benefits:
United States
- Comprehensive medical, dental, and vision benefits, including a company Health Savings Account contribution,
- 401(k): ModMed provides a matching contribution each payday of 50% of your contribution deferred on up to 6% of your compensation. After one year of employment with ModMed, 100% of any matching contribution you receive is yours to keep.
- Generous Paid Time Off and Paid Parental Leave programs,
- Company paid Life and Disability benefits, Flexible Spending Account, and Employee Assistance Programs,
- Company-sponsored Business Resource & Special Interest Groups that provide engaged and supportive communities within ModMed,
- Professional development opportunities, including tuition reimbursement programs and unlimited access to LinkedIn Learning,
- Global presence and in-person collaboration opportunities; dog-friendly HQ (US), Hybrid office-based roles and remote availability for some roles,
- Weekly catered breakfast and lunch, treadmill workstations, Zen, and wellness rooms within our BRIC headquarters.
PHISHING SCAM WARNING: ModMed is among several companies recently made aware of a phishing scam involving imposters posing as hiring managers recruiting via email, text and social media. The imposters are creating misleading email accounts, conducting remote "interviews," and making fake job offers in order to collect personal and financial information from unsuspecting individuals. Please be aware that no job offers will be made from ModMed without a formal interview process, and valid communications from our hiring team will come from our employees with a ModMed email address ([email protected]). Please check senders’ email addresses carefully. Additionally, ModMed will not ask you to purchase equipment or supplies as part of your onboarding process. If you are receiving communications as described above, please report them to the FTC website.
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 Modernizing Medicine, Inc., 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.
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.
Modernizing Medicine, Inc. AI Hiring
Modernizing Medicine, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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
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