This internship focuses on machine learning surrogate models for engineering simulation and measurement. The intern will design, train, and evaluate neural surrogate models to predict engineering responses from simulation or measurement data faster than repeated high-fidelity simulation.
Compensation
(PhD) $51-$56
Responsibilities
Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
Design, train, and evaluate neural surrogate models for engineering response prediction.
Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
Seeking a Machine Learning Scientist II to accelerate drug discovery through advanced analytics and AI, developing predictive models for complex biological and chemical datasets.
Compensation
Base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. $202,000—$238,000 USD
Responsibilities
Develop, implement, and evaluate machine-learning models that support drug discovery questions including compound activity, selectivity, developability, target engagement, and phenotypic screening outcomes.
Perform exploratory data analysis and quality assessment on chemical, biological, imaging, and phenotypic datasets.
Prepare and integrate heterogeneous datasets including chemical structure and screening data, structural biology outputs, molecular simulation outputs, and high-content imaging or morphological profiling data.
Apply appropriate modeling approaches including supervised learning, deep learning, graph-based methods, and ensemble methods.
Use sound validation strategies to assess model performance, robustness, applicability, and limitations.
Requirements
Ph.D.
M.S.
-Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field or M.S. with relevant industry experience.
Providing data analysis and computational support for data-driven efforts to evaluate and improve the MEDLINE indexing product for the National Library of Medicine.
Compensation
The anticipated salary range for this position is $110,000–$135,000 annually.
Responsibilities
Support quantitative analyses evaluating automatic MeSH indexing and its impact on MEDLINE and PubMed users.
Prepare and process PubMed GUI, API, user search and click, bibliographic, indexing, and other relevant datasets for analysis.
Develop and execute scripts for data extraction, cleaning, transformation, aggregation, validation, and analysis using established analytical methods and requirements.
Assist with the development, execution, testing, and maintenance of reproducible data processing and analytical workflows.
Perform exploratory data analysis and apply established statistical and quantitative methods to identify patterns, trends, anomalies, and relationships in data.
An early-career Data Scientist role focused on working with structured and unstructured datasets to discover patterns, develop predictive models, and generate data-driven insights.
Locations (2)
United States
Canada
Responsibilities
Collect, clean, process, and analyze large datasets from multiple sources to identify patterns, trends, and meaningful insights.
Perform exploratory data analysis (EDA) to understand data distributions, relationships, and potential business opportunities.
Develop, train, test, and evaluate machine learning models to solve analytical and predictive problems.
Apply statistical techniques and mathematical methods to interpret data and support evidence-based decisions.
Write efficient code using Python and relevant data science libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
Requirements
Master's degree in Data Science
Senior Associate Aws Data Engineer GCC Advisory Gurgaon
Lead the development of advanced machine learning and statistical models to solve complex healthcare problems and design scalable data pipelines for Snowflake/Data Lake platforms.
Responsibilities
Lead the development of advanced machine learning and statistical models
Establish and promote best practices in modeling, experimentation, and analytical rigor
Convert real-world healthcare challenges into scalable data science solutions
Collaborate with global stakeholders to influence product and business strategy
Mentor and enhance the technical capabilities of the data science team
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