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Biomedical Ai Jobs (NOW HIRING)

This position will play a critical role in helping Nova Biomedical achieve long-term growth objectives through standardized business processes, data-driven decision making, automation, and AI-enabled ...

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... Biomedical Engineering tutors nationally. As an independent contractor on the Varsity Tutors ... Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ...

... Biomedical Engineering tutors nationally. As an independent contractor on the Varsity Tutors ... Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ...

... Biomedical Engineering tutors nationally. As an independent contractor on the Varsity Tutors ... Our AI-powered Tutor Copilot enhances your sessions with real-time instructional support, lesson ...

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Biomedical Ai information

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How much do biomedical ai jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for biomedical ai in the United States is $28.53, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $32.21 per hour, depending on experience, location, and employer.

What is Biomedical AI?

Biomedical AI refers to the application of artificial intelligence technologies in the field of biomedical research and healthcare. This includes using machine learning, deep learning, and data analytics to analyze complex biological data, improve diagnostics, personalize treatment, and accelerate drug discovery. Biomedical AI professionals work at the intersection of computer science, biology, and medicine, developing tools that can help interpret medical images, predict disease outcomes, and uncover new insights from large-scale health data. The field is rapidly growing and is seen as a key driver in the future of healthcare innovation.

What are the key skills and qualifications needed to thrive as a biomedical AI specialist?

To thrive as a Biomedical AI Specialist, you need a solid background in biomedical sciences, computer science, and machine learning, often supported by an advanced degree (e.g., MS or PhD) in a related field. Familiarity with programming languages like Python or R, experience with AI frameworks (such as TensorFlow or PyTorch), and knowledge of healthcare data systems are essential. Strong analytical thinking, problem-solving abilities, and the capacity to communicate complex technical concepts to interdisciplinary teams are vital soft skills. These competencies enable the effective development and deployment of AI solutions that address critical challenges in biomedical research and healthcare delivery.

What are some typical challenges faced by professionals working in biomedical AI, and how can applicants prepare to address them?

Professionals in Biomedical AI often encounter challenges such as integrating diverse data types (e.g., imaging, genomics, clinical records), ensuring data privacy and compliance with healthcare regulations, and translating AI models into clinically actionable insights. To prepare, applicants should familiarize themselves with healthcare data standards, develop strong interdisciplinary communication skills, and stay updated on ethical considerations in medical AI. Experience working with real-world biomedical datasets and collaborating with clinicians or researchers will also be highly valuable.

What is the difference between Biomedical Ai vs Biomedical Data Scientist?

AspectBiomedical AiBiomedical Data Scientist
Required CredentialsDegree in AI, Machine Learning, or related fields; knowledge of biology and healthcareDegree in Data Science, Statistics, or related; strong biology background
Work EnvironmentResearch labs, healthcare tech companies, biotech firmsHospitals, research institutions, biotech companies
Employer & Industry UsageDevelops AI models for medical diagnosis, drug discovery, personalized medicineAnalyzes biomedical data to extract insights, support clinical decisions

Biomedical Ai focuses on developing AI algorithms for healthcare applications, combining AI expertise with biomedical knowledge. Biomedical Data Scientists analyze biomedical data to generate insights and support research. While both roles require a strong understanding of biology and data skills, Biomedical Ai emphasizes AI model development, whereas Biomedical Data Scientists focus on data analysis and interpretation.

How to become a biomedical AI scientist?

To become a biomedical AI scientist, typically a strong background in computer science, machine learning, and biology is required, often through a bachelor's degree in a related field followed by a master's or Ph.D. in biomedical informatics, AI, or a similar discipline. Gaining experience with programming languages like Python, working with biomedical datasets, and understanding healthcare systems are essential, along with staying updated on AI tools and ethical considerations in healthcare. Certifications or specialized training in AI and biomedical data analysis can also enhance qualifications.
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What states have the most Biomedical Ai jobs?

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What are popular job titles for Biomedical Ai?

Popular job titles for Biomedical Ai:

Infographic showing various Biomedical Ai job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 76% Full Time, 20% Part Time, and 3% Contract. Highlights an 62% Physical, 4% Hybrid, and 34% Remote job distribution, with an average salary of $59,333 per year, or $28.5 per hour.

Lead AI Engineer, Biomedical & Vigilance Innovation Software

Durham, NC • On-site

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 10 days ago


Key responsibilities

  • Design, develop, validate, and maintain AI solutions such as machine learning, natural language processing, and generative AI for biomedical and pharmacovigilance use cases.

  • Develop tools and models to support adverse event intake, case triage, coding assistance, duplicate detection, signal prioritization, trend analysis, and risk identification.

  • Collaborate with cross-functional teams to translate scientific and business requirements into production-ready AI applications and ensure their deployment aligns with regulatory and quality standards.


Job description

Lead AI Engineer, Biomedical & Vigilance Innovation Own and drive the development of production-grade AI platforms for pharmacovigilance and safety signal detection

Location: North Carolina, United States

About The Role

United Therapeutics is a biopharma company focusing on rare and cutting‑edge therapies.

We are seeking a Lead AI Engineer with a start‑up mindset to design, develop, and deploy advanced AI solutions that transform how biomedical insights are generated and how safety signals are detected, assessed, and acted upon. The role operates at the intersection of data science, software engineering, and medical safety, applying advanced analytics, machine learning, and automation to create scalable platforms that support proactive risk management and accelerate informed decision‑making across the product lifecycle.

Key Responsibilities
  • Design, build, validate, and maintain machine learning, natural language processing, and generative AI solutions for biomedical and pharmacovigilance use cases.
  • Develop tools that support adverse event intake, case triage, coding assistance, duplicate detection, signal prioritization, and trend analysis.
  • Engineer predictive models to identify emerging risks, patient patterns, and operational bottlenecks.
  • Translate complex scientific and business requirements into production‑ready AI applications.
  • Own model definition, fine‑tuning, and optimization to ensure fit‑for‑purpose AI solutions for the UT patient safety business.
  • Define and execute a bold technology strategy spanning global patient safety, embedding AI, machine learning, and agentic automation across day‑to‑day PV operations, analytics, and signal detection.
  • Drive the architecture, development, and delivery of next‑generation platforms for pharmacovigilance AI initiatives.
  • Integrate structured and unstructured data from safety databases, clinical systems, literature, real‑world evidence, and external repositories.
  • Create staging schemas and mine diverse sources for hidden trends and meaningful insights.
  • Build scalable pipelines for data ingestion, transformation, and quality control.
  • Apply ontology mapping, terminology harmonization, and metadata strategies across MedDRA, WHO Drug, and related standards.
  • Ensure robust data lineage, traceability, and audit readiness.
  • Support modernization of pharmacovigilance and organovigilance systems through AI‑enabled automation and decision support tools.
  • Improve case processing efficiency, medical review, and governance reporting via AI‑enabled solutions.
  • Contribute to next‑generation surveillance models for novel modalities such as xenotransplantation, cell therapy, gene therapy, and organ‑based therapeutics.
  • Develop AI‑enabled dashboards and visualization tools for rapid interpretation of safety trends.
  • Ensure AI models and digital tools align with GxP, privacy, security, validation, and regulatory expectations.
  • Support model governance including performance monitoring, unbiased detection, explainability (XAI), and change control.
  • Maintain documentation for validation, testing, intended use, and lifecycle management.
  • Collaborate with safety, clinical, regulatory, medical affairs, biostatistics, and IT teams.
  • Provide technical guidance to analysts, data scientists, and business partners.
  • Deliver validated AI solutions that create measurable gains in vigilance quality, speed, and insight generation.
  • Improve detection and prioritization of safety signals through advanced analytics.
  • Enhance case processing and review efficiency while preserving quality and compliance.
  • Establish reliable, scalable biomedical data assets for future innovation.
  • Maintain regulatory‑ready governance for AI‑enabled safety systems.
  • Advance our leadership position in responsible AI for the future of medicine.
  • Perform other duties as required.
Minimum Requirements
  • Bachelor’s, Master’s, or PhD in computer science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related field, with the following experience: 8+ years with a Bachelor’s, 6+ years with a Master’s, or 2+ years post‑PhD.
  • 5+ years of experience in AI engineering, machine learning, or advanced analytics within biopharma, healthcare, or regulated industries.
  • 5+ years of hands‑on expertise in AI tools such as Python, R, MATLAB, TensorFlow, PyTorch, and cloud‑based ML platforms.
  • Track record of deploying AI, machine learning, and data science solutions that deliver measurable outcomes in production environments.
  • Entrepreneurial, transformation‑oriented mindset able to move from concept to execution quickly and lead technology‑driven change.
  • Background in AI‑native product development, including agentic AI, LLM‑powered applications, autonomous systems, computer vision, or ML‑driven process optimization.
  • Strong problem‑solving capability and ability to operate in complex matrixed environments.
  • Deep fluency in cloud‑native engineering, platform architecture, and modern software development practices.
  • Experience with external innovation strategies, academic partnerships, and emerging technology investments.
  • Engineering mindset to build production‑grade, compliant, and scalable platforms, not just proof‑of‑concept demos.
  • Excellent communication skills for conveying technical outputs to non‑technical stakeholders and senior leadership.
  • Ability to learn scientific domains quickly and ask insightful questions to drive technology improvements in manufacturing timelines, quality, and cost.
Preferred Qualifications
  • 5+ years of experience with NLP, LLMs, knowledge graphs, or biomedical text mining.
  • Experience with safety systems such as Argus, ArisG, Veeva, or equivalent platforms.
  • Knowledge of pharmacovigilance, clinical development, biomedical data, or healthcare regulations.
  • Exposure to drugs, biologics, devices in the rare disease space or advanced therapeutics.
  • Curiosity about and willingness to develop deep domain expertise in pharma automation workflows, advanced analytics, and risk‑identification methodologies.
  • Hands‑on experience with pharmacokinetic (PK) / pharmacodynamic (PD) modeling and simulation applied to AI in pharmacovigilance.
  • Familiarity with GVP, FDA, EMA, ICH, and data privacy frameworks.
Benefits

This position will be located at our Durham, NC office with a hybrid schedule of 4 days in office and the option to work 1 day each week from home. Eligible employees may participate in the Company’s comprehensive benefits suite, including medical, dental, vision, prescription coverage, employee wellness resources, savings plans (401k, ESPP), paid time off, paid parental leave, disability benefits, and more.

Equal Opportunity Employer

United Therapeutics Corporation is an Equal Opportunity Employer, including veterans and individuals with disabilities. We strive to engage the minds, hearts, and most spirited efforts of each employee. This is a stimulating place to work.

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