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Machine Learning Engineer - Research and Development ROCGJP00027758 A leading biotechnology company is seeking an exceptional Machine Learning Engineer with a passion for building machine learning algorithms and systems that will transform the drug discovery process. The ideal candidate should have extensive experience in developing, deploying, and maintaining models in Research and Development and be able to foster a culture of innovation, collaboration, and excellence across the broader Computational Services organization. The candidate will have experience working in different computing environments, including traditional HPC and AWS, plus experience working with diverse data in the biomedical space, including genetics, genomics, imaging, and clinical data. Machine Learning Engineer Pay and Benefits: • Hourly pay: $70-$90/hr (pay varies based on candidate's experience) • Worksite: Leading biotechnology company (South San Francisco, CA 94080) • W2 Employment, Group Medical, Dental, Vision, Life, Retirement Savings Program, PSL • 40 hours/week, 6 Month Assignment Machine Learning Engineer Responsibilities: • Develop and deploy machine learning models in production environments, working closely with other engineers to ensure solutions are scalable, reliable, and built with best practices. • 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 solve complex problems. • Build solutions that allow stakeholders to interact with and analyze multimodal datasets. Machine Learning Engineer Qualifications: • 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, notably the biomedical space. • Direct experience in Research and Development. • Proficiency in Python. • Experience with Weights and Biases. • Proficiency in MLOps workflows (e.g., familiar with code version control, high-performance compute infrastructures, and machine learning experiment monitoring workflows). • Extensive experience with machine learning frameworks and libraries (e.g., JAX, PyTorch, PyTorch Lightning, Tensorflow). • Strong background in statistics, probabilistic modeling, and data analysis. • Strong communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences as well as interfacing with scientific and engineering leadership • 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 up-to-date with the latest developments in machine learning.