Machine Learning Engineer
3B Staffing LLC, Tahoma, CA, United States
Job Title:
Machine Learning Engineer (Molecular Design)
Location:
Hybrid - Bay Area, CA
Job Description:
We are seeking a
Machine Learning Engineer
with expertise in molecular design to support cutting-edge drug discovery initiatives. The role focuses on developing and optimizing ML workflows for
molecular property prediction
and
generative modeling
to accelerate research and innovation.
Key Responsibilities:
Develop and implement machine learning models for molecular property prediction and generative molecular design.
Collaborate with cross-functional teams to integrate ML workflows into drug discovery pipelines.
Analyze and interpret complex molecular datasets to guide experimental design.
Stay up-to-date with the latest research and publications in molecular modeling and computational chemistry.
Qualifications:
3-5 years of experience in machine learning, computational chemistry, or molecular modeling, or a PhD with relevant publications in molecular design.
Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
Hands-on experience with generative models, predictive modeling, and molecular simulations.
Excellent analytical, problem-solving, and communication skills.
Preferred:
Experience in drug discovery or pharmaceutical research environments.
Familiarity with cheminformatics tools and molecular libraries.
Machine Learning Engineer (Molecular Design)
Location:
Hybrid - Bay Area, CA
Job Description:
We are seeking a
Machine Learning Engineer
with expertise in molecular design to support cutting-edge drug discovery initiatives. The role focuses on developing and optimizing ML workflows for
molecular property prediction
and
generative modeling
to accelerate research and innovation.
Key Responsibilities:
Develop and implement machine learning models for molecular property prediction and generative molecular design.
Collaborate with cross-functional teams to integrate ML workflows into drug discovery pipelines.
Analyze and interpret complex molecular datasets to guide experimental design.
Stay up-to-date with the latest research and publications in molecular modeling and computational chemistry.
Qualifications:
3-5 years of experience in machine learning, computational chemistry, or molecular modeling, or a PhD with relevant publications in molecular design.
Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
Hands-on experience with generative models, predictive modeling, and molecular simulations.
Excellent analytical, problem-solving, and communication skills.
Preferred:
Experience in drug discovery or pharmaceutical research environments.
Familiarity with cheminformatics tools and molecular libraries.