Machine Learning Engineer
Terray Therapeutics is a venture-backed biotechnology company leveraging our vast high-quality data to optimize the path from discovery to transformative therapeutics. Our approach delivers on the promise of computation to revolutionize drug discovery. Through our closed-loop wet lab discovery platform and data-rich AI, we overcome existing constraints on chemical data by systematically mapping the biochemical interactions of massive and diverse small molecule libraries to identify novel therapeutic compounds. Access to this quantitative data at scale allows us to intelligently navigate an infinitely extensible and highly diverse chemical space to efficiently design, discover and optimize small molecule therapeutics. Our internal development programs are focused on immunology. In addition to these programs we also work with leading pharmaceutical companies.
Our integrated computational platform, tNova, harnesses our novel affinity binding technology, tArray, unique resynthesis capabilities, and broad biology infrastructure. tArray is the foundation of our discovery engine. It enables us to screen hundreds of millions of compounds in minutes and return quantitative data on each compound. Vast, high purity, and high diversity compound libraries are rapidly iterated using best-in-class chemistry to unlock chemical insights at the scale necessary to power AI-driven drug discovery.
Position Summary: Terray Therapeutics is seeking a motivated, creative, and experienced Machine Learning Engineer. As an integral member of our data team, the candidate will be responsible for developing and deploying state-of-the-art machine learning models trained on up to billions of small molecule affinity/activity data points in order to accelerate internal drug discovery efforts. The position will report to the Head of Computational and Data Sciences.
The core responsibilities of this job will be:
- Develop multi-task machine learning models for predicting target affinity from molecular structure
- Work with computational chemists to develop novel molecular representations/features
- Develop deep learning anomaly detection methods to flag outliers in our image processing pipeline
- Develop a cloud-based training/inference platform for deploying large-scale production models
- Develop an active learning library design framework for integration of predictive models into an iterative hit discovery cycle
Experience and Qualifications: Given the company's size, anticipated growth and fast-paced environment, the organization requires a machine learning engineer who is thoughtful, high energy and can partner with the broader organization to further enhance our next generation drug discovery capabilities.
Part of Terray Therapeutics' success is nurtured by a hands-on work environment where everyone is accountable, everyone is vested in a vision of excellence, and everyone actively takes part in the success of the business. Terray Therapeutics supports a positive work environment comprised of engaged employees who feel appreciated, recognized and free to be creative.
- PhD in Computer Science, Applied Math, Computational Chemistry, or related quantitative field
- Experience with traditional machine learning methods (e.g., SVM), ensemble methods (e.g., random forest, gradient boosting), as well as deep learning methods (e.g., GNNs)
- Experience with scalable machine learning on GPUs, including applications to large datasets
- Highly proficient in Python and the PyData stack (numpy, pandas, scipy, dask, etc.)
- Highly proficient in scikit-learn, XGBoost, and either PyTorch or TensorFlow
- Familiar with AWS cloud resources (S3, EC2, Batch, SageMaker)
She/he will exhibit the ability to work well under pressure to provide results in a short timeframe. The company is looking for a highly responsive, goal-oriented individual who will bring significant energy and drive to solve complex technical problems and help us achieve our mission to advance human health.