ML Engineer

Advanced Software Talent - South San Francisco, CA

Full-time - Mid Level

Hybrid - South San Francisco, CA

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer position at gRED Computational Sciences (gCS) focuses on developing and deploying machine learning algorithms and systems to enhance the drug discovery process. The role is integral to building a computational and data ecosystem that supports scientific discovery and accelerates decision-making. The ideal candidate will work in a hybrid environment, collaborating with cross-functional teams to solve complex problems and foster a culture of innovation and excellence.

Responsibilities

• Develop and deploy machine learning models in production environments, ensuring scalability and reliability.
• Solve core research engineering challenges, including the design, implementation, and scaling of machine learning algorithms.
• Collaborate with cross-functional teams, including research scientists, computational biologists, and data engineers, to address complex problems.
• Build solutions that enable stakeholders to interact with and analyze multimodal datasets.

Requirements

• B.S. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related field (Graduate degree preferred).
• 4+ years of experience developing and applying ML models in an industry setting.
• Proficiency in Python.
• Experience with Weights and Biases.
• Proficiency in MLOps workflows, including code version control and machine learning experiment monitoring.
• Extensive experience with machine learning frameworks and libraries such as JAX, PyTorch, PyTorch Lightning, and Tensorflow.
• Strong background in statistics, probabilistic modeling, and data analysis.
• Strong communication skills to convey technical concepts to diverse audiences.

Nice-to-haves

• Experience collaborating with external scientific partners, such as academic institutions or industry research groups.
• A passion for solving complex technical problems and a commitment to continuous learning.

Benefits

• Hybrid work environment (3 days onsite, 2 days remote).
• Access to heterogeneous data sources and collaborations with top academic institutions.