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About This Role
What We Do:
Florence software advances cures by helping the world’s most important research sites do their best work. Our solutions are now used by over 30,000 research teams in 70 countries around the world—we’re the most widely deployed site workflow tool in the industry. By the end of the decade, we’ll double the pace at which new medicines get to market by doubling the output of trial site teams. To date, we were named a Deloitte Fast 50 business, G2 Category Leader, an Inc. \& AJC best place to work, and an Inc. 5000 company five years in a row.
At Florence, we are committed to make the world a better place by accelerating research while providing an environment for our employees where they can be happy in their lives, enjoy their jobs, and grow.
What You’ll Bring to the Team:
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The Director of AI Engineering leads the design, development, and delivery of AI\-powered capabilities across Florence products. This is a hands\-on technical leadership role responsible for guiding architecture, mentoring engineers, evaluating emerging AI technologies, and partnering closely with engineering teams to deliver scalable, production\-ready AI solutions. While this role includes people leadership, success is measured by the ability to help teams solve complex technical challenges and accelerate the delivery of AI capabilities.
### You Will:
### *Technical Leadership \& Architecture*
- Lead the technical design and architecture of AI\-powered products and platforms.
- Evaluate and recommend LLMs, AI frameworks, orchestration platforms, and emerging AI technologies.
- Remain hands\-on by building prototypes, validating technical approaches, and helping teams solve complex AI engineering challenges.
- Review architecture, code, and technical designs to ensure scalable, secure, and maintainable solutions.
- Guide engineers on best practices for Generative AI, Agentic AI, Retrieval\-Augmented Generation (RAG), prompt engineering, and model integration.
- Mentor AI engineers through technical coaching, design reviews, and pair problem\-solving.
### *AI Engineering Delivery*
- Lead the development and operationalization of machine learning pipelines, including data preparation, feature engineering, model training, validation, deployment, monitoring, and continuous improvement.
- Drive the best practices and adoption of MLOps practices to enable repeatable, scalable, and reliable machine learning model development and deployment across the organization.
- Work alongside engineering teams to unblock technical challenges and accelerate delivery.
- Partner with Product Management to define and implement AI capabilities that solve customer problems.
- Ensure AI solutions are reliable, observable, performant, cost\-efficient and production\-ready.
- Balance rapid experimentation with engineering quality and operational excellence.
### *AI Platform \& Engineering Excellence*
- Design and Enhance Florence's AI platform, including machine learning pipelines , LLM/model orchestration, vector search, Agentic AI frameworks, Model Context Protocol (MCP), AI gateways, Knowledge retrieval systems, evaluation pipelines, feature stores, model serving infrastructur and observability.
- Establish AI Development Lifecycle (AI DLC) practices, including prompt engineering, evaluation, testing, deployment, monitoring, and governance.
- Establish engineering standards and reusable patterns that enable teams to deliver AI solutions consistently.
- Continuously evaluate new AI tools and frameworks to improve developer productivity and product capabilities.
### *Leadership \& Team Development*
- Lead, mentor, and grow a team of AI Engineers and Machine Learning Engineers.
- Build engineering capabilities across Generative AI, classical Machine Learning, MLOps, and AI platform engineering
- Provide day\-to\-day technical guidance and engineering leadership.
- Foster collaboration, experimentation, and continuous learning across the team.
- Help engineers develop expertise in modern AI technologies and engineering practices.
*Cross\-Functional Collaboration*
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- Partner with Product Management on AI roadmaps and prioritization.
- Work closely with Platform/ Product Engineering, Security, DevOps, QA, and Data Engineering teams.
- Partner closely with Data Engineering and Data Science teams to establish scalable data pipelines, feature engineering practices, and production machine learning workflows.
- Collaborate with Clinical, Customer Success, and Product teams to deliver impactful AI solutions.
- Contribute to engineering planning and technical roadmaps.
- Work closely with Engineering leaders to prioritize AI initiatives and remove delivery risks.
*AI Governance \& Security*
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- Ensure AI systems are secure, reliable, and compliant.
- Implement guardrails, evaluation frameworks, and responsible AI engineering practices.
- Partner with Security and Compliance teams on regulated AI deployments.
- Establish engineering standards for safe AI adoption.
An Ideal Candidate Has:
- 8\+ years of software engineering experience, including significant experience designing, building, deploying, and operating production AI and machine learning systems.
- 4\+ years leading engineering teams in a technical leadership capacity.
- Strong expertise in Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), Agentic AI, prompt engineering, and modern AI application architectures.
- Experience building and scaling production AI systems, machine learning pipelines, and MLOps platforms in cloud\-native environments. .
- Demonstrated ability to evaluate new AI technologies and translate them into practical engineering solutions.
- Strong understanding of production machine learning engineering practices, including model performance monitoring, drift detection, experiment tracking, model versioning, and continuous delivery of ML models.
- Proven experience leading architecture discussions, mentoring technical teams, and influencing engineering direction.
- Excellent communication skills with the ability to engage effectively with executives, product leaders, and engineering teams.
We’ll Be Extra Excited If You Have:
Experience with: Amazon Bedrock, AWS SageMaker, AWS AgentCore, Claude,TensorFlow or PyTorch, Feature Stores, ML Pipeline orchestration tools, OpenAI, Gemini, LangGraph, LangChain, MCP (Model Context Protocol), Kafka, Snowflake, Kubernetes, Docker, Python, MLflow, Vector databases (Pinecone, pgvector, OpenSearch), Healthcare or regulated SaaS environments
Hands\-on Technical Expectations:
- Stay current with advances in Generative AI and AI engineering.
- Build proof\-of\-concepts to evaluate new technologies when appropriate.
- Participate in architecture reviews and technical design sessions.
- Guide engineers through complex implementation challenges.
- Contribute to prototypes or reference implementations for strategic initiatives.
What’s in it for you?
- Do well. We offer a competitive compensation package, medical and dental insurance, and office space in the heart of the city.
- Do good. We insist that health technology is the highest calling for software development. We pride ourselves on working on something bigger than ourselves; helping advance cures and therapies.
- Make the leap. Join our high\-output culture to create innovative, modern, and purposeful software solutions.
Florence supports workplace diversity and does not discriminate on the basis of race, color, religion, gender identity or expression, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, physical disability, or any other protected class.
*Please be cautious of potential recruitment fraud.* *Florence Healthcare will never ask you to pay a fee or download software as part of the interview process with our company. In addition, Florence Healthcare will not ask for your personal banking information until you have signed an offer of employment and completed onboarding paperwork that is provided by our People Operations team. All communications with Florence Healthcare employees will only be sent from @florencehc.com email addresses.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Florence HC, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Florence HC AI Hiring
Florence HC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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