Sr. ML Scientist, Molecular Dynamics & Structure-based Drug Design

As a Senior Machine Learning Scientist at Genentech, your primary responsibilities will include collaborating with experimental scientists to deploy machine learning solutions for small molecule drug discovery, modeling protein-ligand interactions, and developing enhanced sampling workflows to accelerate calculations. Your role will be crucial in integrating machine learning techniques with molecular dynamics, contributing to new hypothesis generation and refining experiments alongside other scientific disciplines.

To qualify for the Senior Machine Learning Scientist role at Genentech, you should have a PhD in a physical sciences or quantitative field, or equivalent industry research experience. Additionally, a record of scientific excellence demonstrated through publications in scientific journals or conferences is essential. Proficiency in Python and experience with biophysical modeling and machine learning frameworks are key qualifications as well.

In your role as a Senior Machine Learning Scientist at Genentech, you will work with various machine learning techniques, primarily focusing on algorithms for modeling protein-ligand interactions and developing workflows for cryptic pocket discovery. You will also build and apply machine learning methods to biophysical datasets, enabling more effective drug discovery processes.

Collaboration is a cornerstone of the Senior Machine Learning Scientist position at Genentech. You will work closely with computational and experimental scientists to deliver machine learning solutions that support small molecule drug discovery. This partnership includes designing and interpreting experiments to validate machine learning-generated hypotheses while contributing to scientific publications and presentations.

Genentech fosters a culture of growth and innovation, providing various opportunities for Senior Machine Learning Scientists to advance their careers. You'll have the chance to contribute to significant research projects, present findings in scientific conferences, and collaborate with experts in the field, all of which can enhance your professional profile and lead to potential leadership opportunities within the organization.

When modeling protein-ligand interactions, I would start by gathering structural data and using molecular dynamics simulations to understand binding affinities. I would implement machine learning algorithms to predict the binding modes and affinities, leveraging existing datasets while iteratively refining the models based on experimental feedback.

I am proficient in several machine learning frameworks, particularly PyTorch and TensorFlow. These tools enable me to leverage deep learning in modeling complex biochemical data, and I have hands-on experience in applying them to enhance existing drug discovery processes.

To ensure reproducibility, I follow best practices in software development by maintaining clear documentation, version control, and making my code available publicly. Additionally, I test my models rigorously and collaborate with experimental scientists to validate my predictions, providing them with detailed methodologies.

I have experience in developing enhanced sampling techniques using OpenMM, which allow for more efficient exploration of the conformational space of proteins. This approach has helped me generate better insights into ligand binding mechanisms and has been instrumental in accelerating drug discovery timelines.

I believe effective collaboration involves open communication and mutual respect for each other's expertise. I actively seek input from experimental scientists during the design of experiments and ensure that my machine learning hypotheses are aligned with their research objectives, thus fostering a productive partnership.

My experience with data preprocessing includes cleaning and normalizing datasets, handling missing values, and transforming data into suitable formats for inducing ML models. I ensure that the training data is representative of the problem to minimize biases and improve model performance.

One challenging project involved developing a machine learning model for accurately predicting protein-ligand binding affinities. The complexity arose from the noisy data and the need to consider dynamic interactions. By utilizing an ensemble approach combining multiple models, we successfully improved the prediction accuracy.

I stay up to date with the latest trends by regularly reading scientific journals, attending relevant conferences, and participating in online forums and workshops. Engaging with the scientific community helps me integrate contemporary advancements into my work.

I am motivated by the potential of machine learning to revolutionize the field of drug discovery and improve healthcare outcomes. Contributing to research that can lead to new therapeutic solutions is deeply fulfilling and drives my passion for this field.

I approach hypothesis generation by closely analyzing the results from my machine learning models and identifying significant patterns or anomalies. I then collaborate with experimental scientists to test these hypotheses, ensuring our findings are grounded in both computational and empirical evidence.