MACHINE LEARNING (ML OPS) SOFTWARE ENGINEER, SMALL MOLECULE DRUG DISCOVERY

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

 The Small Molecule Machine Learning/Artificial Intelligence (ML/AI) team seeks a talented and experienced, self-driven machine learning engineer to lead the design, development, testing, and delivery of software workflows to support ML/AI efforts focused on discovering groundbreaking therapeutics. This position will work closely with ML/AI experts to enable cutting-edge research in small molecule drug discovery. A critical goal will be to enable model development by ML/AI scientists with a workflow that supports testing, validation, dynamic updating, and deployment. You will join an interdisciplinary team of scientists and software engineers at our South San Francisco, CA research headquarters.

The successful candidate will work directly with cross-functional ML/AI and scientific domain expert teams across the small molecule research organization to design, operationalize, and deploy high-quality, sustainable ML/AI solutions addressing impactful scientific challenges. Projects will span diverse technologies, architectures and problem domains, including data provenance, model curation, model performance monitoring, high performance computing, workflow orchestration, deep learning, web services and cloud computing. The common thread is creating innovative ML/AI systems supporting small molecule R&D groups.

Who you are

In hiring new employees, we look for people who are also inspired by our mission and who would fit in well with the collaborative, rigorous and entrepreneurial spirit of the company culture. Because we know that employees are critical to our success in bringing novel medicines to patients, we are dedicated to remaining a great place to work and to providing employees with programs, services and benefits that allow them to bring the best to the business and to their personal lives.

The ideal candidate will satisfy many of the following:

• Masters degree or equivalent in computer science or a related field (Chemistry, Physics, Mathematics, etc.)
• At least 6+ years of experience within the full software development lifecycle from planning through deployment and maintenance
• Demonstrated ability to design, implement, and scale machine learning workflows (ML OPs) and/or scientific computing workflows; including deployment and delivery of production-ready model APIs
• Demonstrated proficiency with version control systems and automated software testing and delivery
• Demonstrated proficiency in idiomatic Python3 and (preferred) one additional language (Java, R, Julia, C++) 
• Proficiency with at least one machine learning lifecycle platform (MLFlow, MetaFlow, TensorFlow, etc.) and/or orchestration platform (Airflow, Dagster, etc.)
• Proficiency with at least one deep learning framework (PyTorch, Keras, JAX, etc.)
• Proficiency with Pandas, SciPy, scikit-learn and (preferred) R/tidyverse and related ecosystems, including relational and non-relational database technologies.
• Evidence of applying and advocating best engineering practices
• Demonstrated leadership commensurate with level of experience, including leading teams with contractors and remote staff.
• The ability to understand complex scientific problems and collaboratively develop reliable solutions that evolve in a rapidly changing research environment
• Proficiency with containerized workflows and architectures preferred
• Experience with high performance computing (HPC) clusters and schedulers preferred
• Experience with Cloud workflows and data strategies preferred
• Able to present your work, both verbally and in writing, to diverse audiences including scientists, technical colleagues, and management
• Undergraduate organic chemistry or equivalent experience is preferred
• Experience with cheminformatics algorithms and software is preferred
• Experience in a drug discovery research organization strongly preferred