Gene Therapy Scientist (PhD) - AI Benchmark Design & Scientific Evaluation (European timezone)

Location: Europe (Remote)

Company: US-headquartered life sciences and AI company

Engagement: Full-time international contractor arrangement

Compensation: Competitive Performance-based earnings

The Opportunity

Are you a gene therapy scientist who enjoys solving complex biological problems, interpreting experimental data, and challenging scientific assumptions?

We are recruiting on behalf of a rapidly growing US-based life sciences technology company working at the intersection of artificial intelligence and biomedical research.

The company develops scientific evaluations that test whether advanced AI systems can accurately interpret biological data, solve complex research problems, and make scientifically defensible decisions.

We are looking for an experienced scientist in gene therapy, gene delivery, or genome editing who can translate their specialist expertise into rigorous scientific challenges for AI systems.

This is not a traditional wet-lab or AI software engineering position. Your value lies in your scientific judgment, understanding of experimental methods, and ability to distinguish genuine biological findings from misleading results.

What You Will Do

• Design challenging scientific evaluation tasks based on real gene therapy research, experimental datasets, and published studies.
• Develop scientifically correct reference answers and assessment criteria to evaluate AI-generated conclusions.
• Investigate complex biological questions related to vector design, delivery efficiency, functional potency, specificity, biodistribution, and therapeutic safety.
• Evaluate experimental and preclinical results, identifying biological signals, technical artifacts, confounding factors, and limitations in the available evidence.
• Identify where AI systems make plausible but scientifically incorrect assumptions, and explain what a knowledgeable researcher would conclude instead.
• Collaborate remotely with scientists and software engineers to develop increasingly sophisticated AI evaluation methods.

You will primarily work within your own scientific area of expertise. You are not expected to be an expert across every gene therapy technology or development stage.

Your Background

We are particularly interested in candidates who have:

• A PhD, or equivalent relevant experience in gene therapy or gene delivery.
• At least 3 years of hands-on research or development experience in gene therapy or gene delivery.
• Recent, first-hand experience designing experiments, analysing biological data, or making research decisions based on experimental evidence.
• Deep expertise in at least one relevant area, such as:
• AAV or lentiviral vector development and characterization.
• In vivo or ex vivo gene therapy.
• Viral or non-viral gene delivery.
• CRISPR-based genome editing and analysis of editing outcomes.
• Gene therapy potency, efficacy, safety, or preclinical development.
• Strong experimental design, critical thinking, and scientific problem-solving skills.
• The ability to explain why a particular scientific method was chosen, what its limitations are, and how to interpret potentially conflicting results.
• Strong written scientific communication skills and professional English proficiency.

Additional Qualifications

Experience with Python, R, bioinformatics, or quantitative analysis would be an advantage, particularly if you have personally developed analytical workflows.

Familiarity with AI tools such as ChatGPT, Claude, or scientific coding assistants is beneficial, but a software engineering or machine learning background is not required.

We welcome applicants from both academia and industry, including experienced postdoctoral researchers with substantial ownership of their scientific work.

What Makes This Role Different?

Rather than conducting experiments at the bench, you will apply your scientific knowledge to evaluate how well AI systems can solve real biological problems.

You will have the opportunity to work on diverse scientific questions, collaborate with an international interdisciplinary team, and contribute to a rapidly developing application of AI in biomedical research.

Your success will depend on the quality of your scientific reasoning, your ability to identify subtle errors, and your capacity to turn complex research problems into clear, verifiable scientific evaluations.

Working Arrangement & Compensation

• Remote collaboration from Europe with a US-based international team.
• Working hours are flexible, with an emphasis on scientific output rather than fixed office hours. Some collaboration and scheduled meetings with US-based colleagues will be required.
• This is a full-time remote opportunity with a US-headquartered company. Candidates may be based across European time zones, including GMT/BST, CET/CEST, and EET/EEST.
• Full-time contractor engagement rather than conventional local employment.
• Performance-based compensation, with annual on-target earnings of approximately USD 140,000-180,000 and potential upside based on output.
• The initial four-week onboarding period is paid at the full on-target rate.
• Opportunities for promotion will be based on scientific contribution, quality, and performance.

The compensation model and contractual conditions will be explained in detail before the technical selection process.

Recruitment Process

The selection process includes a practical scientific assessment, technical interview, and discussion about the working arrangement and mutual expectations.

The assessment will focus on your ability to formulate scientifically rigorous questions and independently evaluate evidence.

Interested?

If you have hands-on experience in gene therapy or gene delivery and enjoy working with challenging scientific data, we would be interested in hearing from you.

Please note:

This role is intended for scientists with direct biological research experience in gene therapy or gene delivery. It is not designed for general software developers, machine learning engineers, or data infrastructure specialists without relevant biological expertise.