Machine Learning 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 at Advanced Software Talent will play a crucial role in the gRED Computational Sciences (gCS) team, focusing on developing and deploying machine learning algorithms and systems to enhance the drug discovery process. This position requires a blend of technical expertise and collaborative spirit to foster innovation and excellence within the organization, while working with diverse data sources in a hybrid work environment.

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 and experience with Weights and Biases.
• Familiarity with MLOps workflows, including code version control and high-performance computing infrastructures.
• 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 both technical and non-technical 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 staying updated with industry trends.

Benefits

• Hybrid work environment (3 days onsite, 2 days remote).
• Access to multidisciplinary research opportunities and collaborations with top academic institutions.