Junior AI Bioinformatics Engineer
Amaris Consulting is seeking a Junior AI Bioinformatics Engineer to support the research and development of generative AI solutions applied to bioinformatics. The role involves building and optimizing AI-driven workflows while collaborating with multidisciplinary teams to enhance biomedical data analysis.
Responsibilities
- Conduct literature reviews on the latest advancements in generative AI, agentic AI, and their applications in healthcare and bioinformatics
- Support the design, development, and evaluation of AI/ML models for biological data workflows (e.g., NGS, precision medicine)
- Implement algorithms and workflows using Python and relevant machine learning frameworks
- Develop and execute benchmarking strategies to assess model performance, robustness, and accuracy
- Contribute to the improvement and optimization of models and workflows
- Ensure reproducibility through proper testing, documentation, and version control practices
- Prepare clear and concise technical documentation, reports, and summaries
- Collaborate with cross-functional teams including data scientists, engineers, and domain experts
- Assist in establishing scalable and reproducible development environments
Skills
- Currently enrolled in or recently completed a Master's degree or PhD in a quantitative field (e.g., Computer Science, Mathematics, Physics, Bioinformatics, or related)
- Strong Python programming skills
- Experience working in Unix/Linux environments, including remote or high-performance computing (HPC) systems
- Hands-on experience in machine learning and deep learning, using frameworks such as PyTorch or similar
- Understanding of model evaluation, validation, and benchmarking techniques
- Strong analytical and problem-solving abilities
- Ability to communicate complex technical concepts clearly
- Collaborative mindset and ability to work in multidisciplinary teams
- Attention to detail and commitment to high-quality deliverables
- Background in statistics, probability, and linear algebra
- Familiarity with generative AI, large language models (LLMs), or agentic AI approaches
- Knowledge of bioinformatics, biological data analysis, or life sciences
- Experience with NGS data or precision medicine applications
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