Machine Learning Engineer

4 weeks ago


Hayward, United States CareerBuilder Full time

We are looking for a Machine Learning Engineer to analyze unique epigenome data and develop computational methods for diagnostic analyses. This role involves developing and optimizing ML/AI algorithms to analyze and interpret complex epigenetic data, including chromatin states and DNA organization.

Primary Responsibilities

Develop and refine machine learning models specifically for epigenetic data interpretation.
Perform feature engineering on cfDNA chromatin data from a proprietary assay to better understand inflammation responses in various populations.
Implement and optimize algorithms to understand chromatin modifications and their impact on gene expression.
Collaborate with wet-lab scientists and bioinformaticians to perform end-to-end analysis of experiments including the design, processing, QC, analysis, and interpretation of the NGS results.
Contribute to the development and implementation of primary analysis software.
Contribute to building a culture at a fast-growing start-up.

Qualifications

MA/MS or PhD degree in Computer Science, Statistics, Bioinformatics, Computational Biology, or related field with a focus on Machine Learning.
3+ years of experience in applying machine learning to biological data.
Strong proficiency in the Python development stack (pandas, numpy, matplotlib, scikit-learn, statsmodels) and the ML/AI libraries (TensorFlow/Keras, PyTorch, scikit-learn).
Strong proficiency with common ML algorithms (random forest, logistic regression, SVM, others) and common neural net architectures (CNNs, RNNs, transformers).
Experience with high-dimensionality reduction techniques (PCA/SVD/t-sne/auto-encoding neural nets).
Familiarity with R (tidyr, dplyr, ggplot2) strongly preferred.
Past research in NGS, RNA-seq, whole transcriptome analysis and related methods a strong plus.
Familiarity with epigenomic data analysis tools and techniques a strong plus.
Familiarity with cloud computing environments (AWS preferred) a strong plus.
Familiarity with common CI/CD frameworks (Jenkins/airflow/etc) as well as experience with GitHub.
Excellent data visualization skills, emphasizing the ability to employ data analysis to convey a scientific position.
Strong project management skills with the ability to juggle multiple diverse projects.
Please note, that this is a hybrid role.

Join our team and contribute to pioneering work in applying machine learning to epigenomics, advancing our understanding of gene regulation and chromatin architecture.
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