Remote RNA-Seq ML Engineer Intern

X10e Inc.; California; On-site; Full time

Job description

We are seeking a highly motivated Machine Learning Intern to assist in developing AI models for analyzing RNA-seq data.
This internship offers a unique opportunity to gain hands-on experience at the intersection of artificial intelligence and biological research.
You will contribute to building computational pipelines that uncover meaningful patterns in gene expression data and provide insights into biological processes.
Key Responsibilities:

Data Preparation and Preprocessing:

Assist in cleaning, normalizing, and preparing RNA-seq datasets for machine learning analysis.

Integrate metadata and other contextual information into datasets.

Model Development and Training:

Implement machine learning models (e.g., CNNs, RNNs, Autoencoders) for pattern detection and dimensionality reduction.

Support predictive modeling to identify critical features in RNA-seq data.

Analysis and Interpretation:

- Perform exploratory data analysis and assist in visualizing results.

- Collaborate with team members to interpret model outputs and refine approaches.

Documentation and Reporting:

- Document workflows, results, and insights clearly and concisely.

- Present findings in team meetings and contribute to project reports.

Required Skills:
- Enrolled in a Bachelor's, Masters, or Ph.D. program in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field.
- Strong interest in applying machine learning to biological data.
- Basic knowledge of RNA-seq data analysis or transcriptomics.
- Experience with programming languages such as Python or R.
- Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

- Excellent analytical and problem-solving skills.

Preferred Skills:
- Experience with data visualization tools (e.g., Matplotlib, Seaborn, ggplot2).
- Familiarity with dimensionality reduction techniques (e.g., PCA, t-SNE).
- Knowledge of neural networks and deep learning architectures (e.g., CNNs, RNNs).
- Understanding of biological systems and gene expression concepts.

What We Offer:
- Mentorship and hands-on experience in cutting-edge AI applications for biological research.
- Flexible working arrangements, including remote options.
- Opportunity to contribute to real-world projects with potential for publication or recognition.
- A collaborative and supportive learning environment.

How to Apply:
Please send your resume, cover letter, and a brief description of a relevant project or coursework to [e-mail].