Interested in this AI/ML Engineer role at NeoGenomics?
Apply Now →About This Role
Are you motivated to participate in a dynamic, multi-tasking environment? Do you want to join a company that invests in its employees? Are you seeking a position where you can use your skills while continuing to be challenged and learn? Then we encourage you to dive deeper into this opportunity.
We believe in career development and empowering our employees. Not only do we provide career coaches internally, but we offer many training opportunities to expand your knowledge base! We have highly competitive benefits with a variety HMO and PPO options. We have company 401k match along with an Employee Stock Purchase Program. We have tuition reimbursement, leadership development, and even start employees off with 16 days of paid time off plus holidays. We offer wellness courses and have highly engaged employee resource groups. Come join the Neo team and be part of our amazing World Class Culture!
NeoGenomics has an opening for a Commercial Strategic Initiatives and AI Integration Manager who wants to continue to learn in order to allow our company to grow. The ideal candidate will be located remotely within the United States.
Now that you know what we're looking for in talent, let us tell you why you'd want to work at NeoGenomics:
As an employer, we promise to provide you with a purpose driven mission in which you have the opportunity to save lives by improving patient care through the exceptional work you perform. Together, we will become the world's leading cancer reference laboratory.
Position Summary
The Commercial Strategic Initiatives and AI Integration Manager is responsible for defining and advancing the artificial intelligence (AI) strategy for the NeoGenomics Commercial organization, spanning both current state assessment and future state direction. This role establishes where and how AI creates measurable commercial advantage, prioritizes investment against the organization's growth objectives, and ensures that efforts are connected across Commercial functions rather than pursued in isolation.
The role works cross-functionally with Commercial leadership, Digital Technology Solutions (DTS), Legal, and Compliance to ensure alignment between commercial ambition, technical feasibility, and the obligations that come with operating in a regulated healthcare environment. It also serves as the Commercial organization's point of contact for AI across NeoGenomics, representing Commercial in enterprise AI initiatives and governance.
This role requires a strategic thinker with direct AI or advanced analytics experience and the ability to lead cross-functional initiatives without direct authority to achieve measurable outcomes.
Core Responsibilities
- Develop and maintain a clear assessment of AI adoption across the Commercial organization, including tools in use, pilots underway, spend, capability gaps, and duplicated effort.
- Define the multi-year AI strategy for Commercial, including a prioritized roadmap, sequencing, and a defensible position on what the organization builds, buys, or defers.
- Serve as the connection point for AI across Commercial functions, ensuring each function has a clear path to applying AI against its own priorities and that the organization learns from every effort.
- Serve as the Commercial point of contact for AI across NeoGenomics, representing Commercial priorities, requirements, and constraints in enterprise discussions and bringing enterprise developments back into Commercial planning.
- Represent Commercial on enterprise project and governance teams aligned to AI initiatives, ensuring Commercial needs are reflected in decisions on tooling, data, and policy.
- Establish consistent criteria for evaluating AI opportunities, size the value, and bring recommendations to the Commercial Leadership Team.
- Lead a focused set of proof-of-concept initiatives that validate the strategy in practice, then transition successful efforts to the owning function with a plan for scale.
- Partner with Commercial Training to build AI fluency across the Commercial organization and drive at-scale adoption.
- Partner with DTS, Legal, and Compliance to ensure the Commercial approach to AI meets company standards for data governance, privacy, and protected health information (PHI).
- Monitor the vendor landscape, competitor activity, and the evolving regulatory environment as it affects commercial use of AI in diagnostics and translate those developments into recommendations for senior leadership.
- Communicate strategy, progress, and performance metrics to senior leadership and key stakeholders.
- Establish and execute the AI strategy that advances the growth objectives of the NeoGenomics Commercial organization.
- Represent the Commercial organization in enterprise AI initiatives and governance.
- Lead additional projects and strategic initiatives assigned by the Senior Director, Commercial Strategy.
Experience, Education and Qualifications
- Bachelor's Degree required.
- Master's Degree preferred.
- 8+ years of experience in strategy, commercial operations, technology strategy, consulting, or a related field.
- 4+ years of experience leading cross-functional initiatives without direct authority.
- Direct experience with AI or advanced analytics, including a working understanding of current capabilities and their limitations.
- Strong strategic thinking and problem-solving capabilities, with the ability to translate an ambiguous and fast-moving environment into a clear plan with explicit trade-offs.
- Ability to build and maintain strong relationships with executives, partners, and stakeholders.
- Proven ability to work effectively with and influence peers, sales teams, and management.
- Data-driven mindset with the ability to leverage analytics to drive business decisions.
- Exceptional communication and interpersonal skills, with the ability to communicate effectively with both executive and technical audiences.
- Proven oral, written, telephone, and presentation skills.
- Proficiency with Microsoft Office programs.
- Strong organizational skills and attention to detail.
- Ability to work independently and in a collaborative team environment.
Preferred Qualifications
- Familiarity with oncology, hematology, and diagnostics businesses.
- Knowledge of clinical and/or laboratory market environments and products.
- Experience defining an enterprise or functional AI strategy in a regulated industry.
- Familiarity with data privacy and regulatory considerations associated with health data.
- Experience in commercial enablement functions such as analytics, training, or commercial operations.
Travel Required
Travel as needed.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NeoGenomics, 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 in Demand for This Role
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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
NeoGenomics AI Hiring
NeoGenomics 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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