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About This Role
AskBio Inc., a wholly owned and independently operated subsidiary of Bayer AG, is a fully integrated gene therapy company dedicated to developing life-saving medicines and changing lives. The company maintains a portfolio of clinical programs across a range of neuromuscular, central nervous system, cardiovascular, and metabolic disease indications with a clinical-stage pipeline that includes investigational therapeutics for congestive heart failure, limb-girdle muscular dystrophy, multiple system atrophy, Parkinson’s disease, and Pompe disease. AskBio’s gene therapy platform includes Pro10™, an industry-leading proprietary cell line manufacturing process, and an extensive array of capsids and promoters. With global headquarters in Research Triangle Park, North Carolina, and European headquarters in Edinburgh, Scotland, the company has generated hundreds of proprietary capsids and promoters, several of which have entered pre-clinical and clinical testing.
Our vision: Pioneering science to create transformative molecular medicines.
Our mission: Lead innovative science and drive clinical outcomes to transform people's lives.
Our principles:
- Advance innovative science by pushing boundaries.
- Bring transformative therapeutics to patients in need.
- Provide an environment for employees to reach their fullest potential.
Our values:
- Be a Pioneer. We are not afraid of the impossible and to innovate to make gene therapies accessible to those in need.
- Cultivate Collaboration. Strive to be the best teammate, actively listen, openly communicate, and embrace diverse points of view.
- Embrace Responsibility. We are humbled by the enormity of our mission. We hold a relentless commitment to advance science and clinical outcomes for our patients, families, and caregivers.
- Raise the Bar. Continuously drive improvements and efficiencies. Seek and provide constructive feedback. Have a bias for learning and action.
- Act with Uncompromising Integrity. Be honest, transparent, and committed to doing what’s right in every situation. Make clear commitments and follow through.
Position Summary
We are seeking a curious, tech-savvy intern to join our Clinical Operations team and explore how artificial intelligence (AI) can be applied to drive efficiency, accuracy and speed in clinical trial processes. This internship offers the opportunity to work at the intersection of clinical research and digital innovation, helping to shape how gene therapy programs are executed globally.
This position is office-based in RTP, NC and will report to the SVP of Clinical Operations.
Job Responsibilities
- Analyze current Clinical Operations workflows to identify areas where automation or AI tools could enhance efficiency
- Support pilot projects applying AI to areas such as document management, data quality review, patient enrollment tracking and site communication
- Assist in developing dashboards, algorithms, or predictive models that optimize trial planning and execution
- Collaborate cross-functionally with Data Science, Clinical Development, and IT teams to evaluate and implement emerging technologies
- Document findings and share recommendations for scalable process improvements
- Stay current on AI and machine learning trends in clinical research and summarize insights for the team
- Prepare and deliver a presentation at the end of the program to the Clinical Operations team on your findings
Minimum Requirements
- Currently pursuing a Bachelor’s Degree in Biomedical Engineering, Data Science, Computer Science, Clinical Research, Program/Project Management or related field
- Strong analytical and problem-solving skills with an interest in process optimization
- Familiarity with AI concepts, tools, and current trends in the healthcare sector
- Excellent oral and written communication skills; ability to translate technical findings into practical recommendations
- Enthusiasm for innovation in life sciences and improving patient outcomes through technology
- Proficiency with MS Office Suite (Word, Excel, PowerPoint, Outlook, Teams)
- Self-motivated with a solution driven mindset
- Strong sense of accountability and ability to prioritize multiple tasks
- Available to work full-time during the summer months (May-August) at an AskBio office
Preferred Education, Experience and Skills
- Familiarity with machine learning algorithms and AI frameworks
- Ability to perform statistical analysis and interpret results to inform decision making
- Basic understanding of clinical operations, pharmaceutical regulations and patient care processes
- Flexible and willing to support a variety of tasks for the Clinical Operations team
*AskBio Inc. (AskBio) is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status or any other protected status prohibited under Federal, State or local laws. All employment decisions are based on valid job‐related requirements. If you are a qualified individual with a disability or a disabled veteran and are unable or limited in your ability to use or access our website, you may request a reasonable accommodation to express interest in a specific opening by calling us at (919) 561-6210 or sending us an email at* *[email protected]* *.*
*Agencies: Please do not contact any employee at AskBio about this requisition. Any resume submitted by a recruitment agency to any employee at AskBio, through any medium, will be deemed the sole property of AskBio* unless *the agency was engaged by AskBio Talent Acquisition team to recruit for that position. All agencies must have a prior executed service agreement with AskBio prior to any search engagement. If a candidate who was submitted outside of the AskBio agency process is hired by AskBio, no fee or payment of any kind will be paid to the agency.*
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 3,897 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Asklepios Biopharmaceutical 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 $154,000 based on 8,743 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $85,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Asklepios Biopharmaceutical Inc AI Hiring
Asklepios Biopharmaceutical Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Durham, NC, US.
Location Context
Across all AI roles, 16% (615 positions) offer remote work, while 3,251 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 3,897 open positions tracked in our dataset. By seniority: 111 entry-level, 1,958 mid-level, 1,413 senior, and 415 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (615 positions). The remaining 3,251 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 3,897 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,733), Data Scientist (273), AI Software Engineer (271). 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 (111) are outnumbered by mid-level (1,958) and senior (1,413) 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 415 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (615 positions), with 3,251 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (2,064 postings), Aws (1,085 postings), Azure (867 postings), Rag (865 postings), Gcp (697 postings), Pytorch (650 postings), Prompt Engineering (597 postings), Kubernetes (499 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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