Job Title: Computational Biology Expert (PhD)

Job Type: Contractor

Location: Remote

Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

Key Responsibilities:

1. Leverage your biology domain expertise to evaluate, annotate, and benchmark AI systems in real-world computational biology use cases.
2. Assess the accuracy, relevance, and performance of AI-generated outputs in genomics, transcriptomics, and systems biology scenarios.
3. Apply advanced analytical reasoning to critically review scientific workflows and pipelines, identifying strengths and areas for improvement.
4. Collaborate with interdisciplinary teams, providing feedback on AI model performance based on biological context.
5. Document observations and findings with clarity, highlighting scientific rationale and actionable insights.
6. Communicate complex concepts effectively through both written and verbal channels to technical and non-technical stakeholders.
7. Stay current on emerging trends in computational biology, bioinformatics, and machine learning in biology.

Required Skills and Qualifications:

1. PhD in Biology, Bioinformatics, or a closely related field, or equivalent industry/research experience.
2. Proven experience in data-driven biological research and computational analysis workflows.
3. Proficiency with scripting (Python, R, Bash) and scientific tools such as BWA, GATK, STAR, Salmon, Seurat, or Scanpy would be ideal.
4. Deep understanding of genomics, transcriptomics, structural biology, or systems biology is desirable.
5. Excellent scientific reasoning, analytical, and problem-solving abilities.
6. Strong written and verbal communication skills, with the ability to articulate findings and collaborate effectively.
7. Comfort working remotely and contributing to distributed teams.

Preferred Qualifications:

1. Experience with AI/ML or LLM evaluation in a biology context.
2. Hands-on experience with NGS pipelines, CRISPR workflows, AlphaFold, or PyMOL.
3. Published research or contributions to open-source projects in computational biology or bioinformatics.