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About This Role
Location: Remote
If you are interested in the below position, please send your resume to [email protected] and REFERENCE THE POSITION TITLE within the subject line.
Organizational Overview
TG Therapeutics is a fully integrated, commercial stage, biotechnology company focused on the acquisition, development and commercialization of novel treatments for B\-cell diseases. In addition to a research pipeline including several investigational medicines, TG has received U.S. Food and Drug Administration (FDA) approval for BRIUMVI® (ublituximab\-xiiy), for the treatment of adult patients with relapsing forms of multiple sclerosis (RMS), to include clinically isolated syndrome, relapsing\-remitting disease, and active secondary progressive disease, as well as approval by the European Commission (EC) and the Medicines and Healthcare Products Regulatory Agency (MHRA) for BRIUMVI to treat adult patients with RMS who have active disease defined by clinical or imaging features in Europe and the United Kingdom, respectively. TG Therapeutics is headquartered in Morrisville, North Carolina. For more information, visit www.tgtherapeutics.com.
Role
Reporting directly to the VP of Information Technology, the Director, Enterprise AI \& Enablement will lead the strategy, governance, and operationalization of artificial intelligence capabilities across TG Therapeutics. This role is responsible for driving secure and effective enterprise AI adoption, enabling measurable productivity gains, and scaling AI usage across all business functions. The Director will partner closely with IT leadership, Information Security, business stakeholders, and external vendors to establish and mature TG’s enterprise AI program.
Key Responsibilities
Strategy \& Governance
- Develop and execute TG’s enterprise AI enablement roadmap aligned with business priorities and IT strategy
- Establish governance frameworks, usage policies, and risk controls for AI tools across the organization
- Partner with InfoSec and Infrastructure teams to ensure AI adoption meets security and compliance requirements
- Maintain and evolve the AI risk assessment framework in alignment with regulatory and corporate standards
- Define and evolve TG’s enterprise AI operating model, standards, and best practices
- Serve as a strategic advisor to leadership on emerging AI trends, opportunities, risks, and enterprise adoption strategies
Platform \& Tool Enablement
- Own the evaluation, procurement, and lifecycle management of enterprise AI platforms (e.g., Claude, Microsoft Copilot, ChatGPT)
- Drive the adoption, enablement, and optimization of AI integrations across productivity tools including Microsoft 365 (PowerPoint, Excel, Teams, Outlook)
- Identify and close gaps between AI platform capabilities and measurable business productivity outcomes
- Lead the deployment of AI enablement solutions across IT and business functions
- Identify opportunities to improve business processes and operational efficiency through AI\-driven solutions and workflow automation
Business Partnership \& Adoption
- Collaborate with business leaders across Commercial, R\&D, Clinical, and Corporate functions to identify AI use cases that drive meaningful outcomes
- Develop and deliver training, enablement programs, and best practices to drive effective AI adoption across the organization
- Serve as the internal subject matter expert and advocate for responsible AI use
- Lead organizational AI adoption and change management initiatives to drive sustainable and effective use of AI technologies across the enterprise
- Develop KPIs and success metrics to measure enterprise AI adoption, effectiveness, and business value
- Track and communicate ROI and business impact of AI enablement initiatives
Team \& Vendor Management
- Build and mentor a team of AI enablement specialists as the practice scales
- Manage relationships with AI vendors, implementation partners, and managed service providers
- Evaluate emerging AI technologies and make recommendations to senior leadership
Professional Experience/Qualifications
Required
- 10\+ years of experience in IT, with 5\+ years in a leadership or management position, with at least 3 years in an AI, enablement, or digital transformation role
- Experience in the pharmaceutical, biotech, or life sciences industry
- Demonstrated experience deploying and managing enterprise AI platforms at scale
- Strong understanding of AI governance, data privacy, and security considerations
- Experience working cross\-functionally with business stakeholders to drive technology adoption
- Excellent communication and presentation skills, with the ability to translate technical concepts for non\-technical audiences
Preferred
- Familiarity with GxP environments and regulatory compliance considerations
- Experience with Microsoft 365 Copilot, Claude, or similar enterprise AI platforms
- Master’s degree or MBA
Education
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field
*Applicants must be currently authorized to work in the United States on a full\-time basis. The company does not sponsor employment visas for this position.*
*TG Therapeutics is an equal employment opportunity employer, and does not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, age, disability, national origin, ancestry, genetic information, military or veteran status, pregnancy or pregnancy\-related condition or any other protected characteristic.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TG Therapeutics, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TG Therapeutics, Inc. AI Hiring
TG Therapeutics, 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 $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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