Interested in this AI/ML Engineer role at axogen?
Apply Now →Skills & Technologies
About This Role
Axogen is committed to building and maintaining a strong and gratifying company culture that fosters professional growth. Our hands-on and personal approach makes transitioning to a new job a seamless and enjoyable process. Most benefits are effective on day one!
Axogen is an equal opportunity employer and does not discriminate against applicants on the basis of race, color, creed, religion, ancestry, age, sex, marital status, national origin, disability or veteran status.
Why You’ll Love Working At Axogen
- Friendly, open, and fun team culture that values unique perspectives
- Company-wide dedication to profoundly impacting patients’ lives
- Comprehensive, high-quality benefits package effective on date of hire
- Educational assistance available for all employees
- Matching 401(k) retirement plan
- Paid holidays, including floating holidays, to be used at your discretion
- Employee Stock Purchase Plan
- Referral incentive program
Axogen Mission and Business Purpose Our business purpose is to restore health and improve quality of life by making restoration of peripheral nerve function an expected standard of care. We aim to lead the markets we serve by always requiring the solutions we offer patients and caregivers provide an improved benefit-to-risk profile as compared to existing standards of care. To ensure we deliver improved benefit-to-risk solutions, we will guide and expect the market and design requirement specifications underlying our engineering, business development, and clinical research activities, objectively target advancements in standards of care.
Job Summary of the Director, Enterprise Data & AI Strategy The Director, Enterprise Data & AI Strategy is a strategic, is a visionary enterprise leader responsible for defining and executing an AI strategy with global data that accelerates digital transformation, enables insightful decision-making, and drives operations excellence. This role will oversee the framework and integration of AI capabilities, enterprise data and automation to deliver that business impact shaping the organizations long term data vision and governance model. The Director will report directly with the VP of IT, partner closely with executive leadership (in particular the Finance, to ensure data and AI investments align with strategic business goals, innovation and priorities.
Requirements Of The Director, Enterprise Data & AI Strategy
- Bachelor’s degree in computer science, Information Systems, Computer Science, Data Science, Engineering or related field; master’s degree preferred.
- Minimum 12 years’ experience in data analytics, AI/ML, or automation roles in life sciences, biotechnology, healthcare, or manufacturing industries.
- Minimum 8 years of management experience in leading cross-functional teams and projects.
- Proven experience in building and scaling data teams and platforms.
- Strong knowledge of data governance frameworks, BI tools (e.g., Power BI, Tableau), and cloud data platforms (e.g., Azure, AWS).
- Experience building data warehouse or data marketplace and integrating data sources from multiple source systems using custom APIs, common middleware platforms, and advanced SQL.
- Hands-on experience with AI/ML technologies, RPA tools, and building out automation frameworks.
- Demonstrated experience in IT project management and business analysis with expertise in at least one of the following functional processes: Procure to Pay, Demand to Build, Order to Cash, or Financial processes.
- Strong understanding of enterprise applications including ERP, CRM, QMS, LIMS, HRIS, etc.
- Excellent communication skills with the ability to engage effectively with executives, business stakeholders, and technology partners.
- Proven ability to lead teams in a data-driven manner, utilizing metrics and project reviews.
- Familiarity with the strategic deployment of IT systems and the development of standard operating procedures for end-user training and adoption.
Responsibilities Of The Director, Enterprise Data & AI Strategy The specific duties of the Director, Enterprise Data & AI Strategy include but are not limited to:
- AI & Automation Strategy: Focus on shaping the vision and transformation of AI and Data Management. Owns the enterprise AI & automation strategy by identifying innovation opportunities, scaling AI use cases, and embedding automation across business processes - develops and executes a roadmap for AI and intelligent automation initiatives focused on driving operational efficiency and ROI. Evaluates and implements emerging technologies, including generative AI, RPA, and machine learning models such as Power Automate, UI Path, Automation Anywhere, Fabric, or others. Collaborate with executive sponsors to prioritize AI investments.
- Team Leadership and Development: Build and lead a high-performing team of data scientists, database administrators, AI/ML and automation engineers. Foster a culture of innovation, collaboration, and continuous improvement.
- Project Governance: Oversees and manages the intake of project and enhancement requests, provides initial time, resource and cost estimates, and aligns priorities with leadership and key stakeholders. Oversees the delivery of MDM, AI, and Automation projects and enhancements, ensuring they align with the company’s strategic goals and are delivered on time and within budget. Monitors project progress and takes corrective actions as needed to mitigate risks while ensuring successful outcomes.
- Data Strategy & Governance: Oversees the enterprise data strategy & governance by establishing enterprise standards for data ethics and AI governance. Defines and implements enterprise-wide data governance policies, standards, and best practices. Ensure data quality, integrity, security, and compliance across all systems following CSV process requirements. Partners with executive leadership to ensure compliance with evolving regulations and frameworks. (GxP, GDPR, AI ACT)
- Business Analysis: Develops a deep understanding of data flows supporting core business processes; Order to Cash, Procure to Pay, Demand to Build, Record to Report, Hire to Retire, Regulatory and Compliance. Reviews and approves requirements written by the business analysis team. Collaborates with business analysts to ensure clear and comprehensive documentation of project needs.
- Analytics & Business Intelligence: Shapes enterprise BI and analytics roadmap, enabling predictive insights and AI driven decision making for the C-suite and business units.
- Governance, Risk, and Compliance: Possesses a deep understanding of IT General Controls, GxP regulations and CSV standards. Ensures that all projects adhere to SOX, GxP compliance/Data Integrity requirements and partner with Quality to maintain compliance. Collaborates with cross-functional teams to address and resolve Quality or Audit Findings.
- Stakeholder Management: Builds and maintains strong relationships with key business stakeholders, ensuring clear communication and alignment of data initiatives with business needs and budgets. Communicates complex technical concepts to non-technical stakeholders effectively.
- Budget & Resource Allocation: Partnering closely with the VP of IT, this person will owns the data and AI portfolio budget, to optimize spend across global programs. Manages resource allocation effectively, balancing project needs with team capabilities and workload.
- Process Improvement: Continuously evaluates and improves core Procure to Pay, Demand to Build, Order to Cash, and Financial processes, incorporating best practices and innovative solutions.
- Vendor Management: Develops strategic partnerships with global technology vendors, consulting partners, and AI innovators to advance enterprise capabilities. Oversee relationships with key vendors, ensuring the effective delivery of services, visibility into application development roadmaps, advocating for business user enhancements, and alignment with business goals.
Location 111 West Oak Ave., Tampa, FL 33602
Benefits/Compensation This position is eligible for an annual bonus.
Benefits offered for this position include Health, Dental, Vision, Matching 401K, Paid Time Off, 9 Paid Holidays + 3 Floating Holidays, Dependent Care Flexible Spending Accounts, Medical Flexible Spending Accounts, Tuition Reimbursement, Paid Parental Leave, Paid Caregiver Leave, Basic Life Insurance, Supplemental Life Insurance, Employee Stock Purchase Plan, and Disability Insurance, as described in more detail in summary plan descriptions.
Salary Range
$168,828—$211,034 USD
Company Statements Axogen follows healthcare system guidelines with respect to credentialing, vaccinations and other employment/compliance related requirements, as well as CDC guidance. Axogen reserves the right to amend its policies from time to time in its sole discretion.
As AI is becoming a common tool throughout the application process, we want to be clear about its appropriate use at Axogen. Using AI to support resume writing, research or interview preparation is perfectly acceptable, provided the content is accurate and genuinely represents your qualifications and skills. For other key parts of your interview process, however, it is important that the ideas, communication, and work you share reflect your own voice, experiences, and thinking. We ask that you participate in our live interviews and complete any assessments without AI assistance unless instructions explicitly indicate otherwise or a specific exception is discussed and approved in advance. This approach ensures fairness, celebrates your individuality, and allows your authentic perspective to shine. Behaviors that do not align with these guidelines may result in disqualification from the hiring process or termination of employment if later discovered. We appreciate your understanding and look forward to learning about the unique contributions only you can bring to Axogen.
No agency solicitation. We kindly request that recruiting agencies, staffing firms, and third‑party vendors do not contact us regarding this posting. Unsolicited résumés will not be considered.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At axogen, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
axogen AI Hiring
axogen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.