About the position
At Atomic AI, we build artificial intelligence to pioneer new frontiers in drug discovery. Our unique R&D platform, an early version of which was featured on the cover of Science, provides new strategies to treat previously undruggable diseases by targeting RNA. We continue to advance this platform by developing new machine learning (ML) methods and unique foundation models fueled by our large-scale, in-house experimental data collection. We are an interdisciplinary team of scientists and engineers and believe our people are our greatest strength and the key to our success. As a full-time Data Scientist on the Machine Learning team, you will work closely with scientists and engineers to apply and advance our R&D technology platform for RNA structure prediction, target identification, and early stage drug discovery. You will lead the curation of RNA-focused datasets for ML training and validation, discover statistical patterns in our large-scale datasets evaluating RNA structure and RNA-small molecule interactions, and devise and implement new strategies to test the accuracy of our ML models. Your analysis will guide the development of improved ML models and the targeted acquisition of new experimental data. This is a hybrid position with three days in-person at our South San Francisco office.
Responsibilities
- Provide RNA biology and RNA structure expertise on the ML team.
- Enable and apply our RNA-structure platform to prioritize RNA targets for small-molecule therapies and advance structure-based drug discovery.
- Generate insights from datasets on RNA structures and small molecule interactions (e.g. chemical probing, RNA-SM screens) by conducting statistical analyses, interpreting biological noise, and applying RNA domain expertise.
- Curate RNA datasets for training of ML models, help evaluate model performance, and provide directions for improvement.
- Inform scientific questions and ML model development in early-stage RNA drug discovery.
- Collaborate with the ML Data and Drug Discovery teams and shape the design of experimental assays on RNA structure and RNA-SM interactions.
Requirements
- Ph.D. in Computational Biology, Bioinformatics, Statistics, Biophysics, or related field, or equivalent experience.
- Expertise in RNA biology and biochemistry, RNA-protein interactions, and RNA structure.
- Proficiency in Python for data curation and analysis at scale, and fluency with libraries for data analysis (NumPy, pandas) and applied ML (scikit-learn).
- Strong programming background, familiarity with Unix and comfort with using external software packages.
- Strong foundation in statistics and experience with conducting statistical analysis of large-scale datasets.
- Excellent presentation and writing skills, able to clearly communicate technical information to colleagues.
Nice-to-haves
- History of scientific achievement, e.g. as evidenced by publication of impactful papers.
- Conceptual understanding of ML model development and evaluation, and experience using ML models.
- Experience with computational structural biology tools for modeling RNA secondary and tertiary structure (e.g. Rosetta, AlphaFold, RNAfold).
- Understanding of RNA-SM interactions, including familiarity with structural properties of binding sites and experimental methods for evaluating binding.
- Proficiency with pipelines for next-generation sequencing dataset processing.
- Exposure to high throughput experimental assays for evaluating RNA structure and screening RNA-SM interactions.
Benefits
- Salary Range: $135,000/year to $165,000/year + equity + benefits.
