

R&D Partners
Machine Learning Engineer (Pharmaceuticals)
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer (Pharmaceuticals) on a 12-month contract in South San Francisco, California, with a pay rate of $97,801-$158,932. Key skills include machine learning libraries, Bayesian optimization, and experience in molecular design. A PhD or MS with 3+ years in a quantitative field is required.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
722
-
ποΈ - Date
October 8, 2025
π - Duration
More than 6 months
-
ποΈ - Location
On-site
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π - Contract
Unknown
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π - Security
Unknown
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π - Location detailed
South San Francisco, CA
-
π§ - Skills detailed
#GitHub #PyTorch #R #Computer Science #Supervised Learning #ML (Machine Learning) #Deep Learning #Libraries
Role description
Pharmaceutical company is looking for a talented Machine Learning Engineer to develop structural and machine learning-based methods for molecular design. The successful candidate will manage projects deploying new techniques for machine learning-based molecular optimization for the analysis and design of small and large molecule drugs within target-driven design campaigns. Special focus will be given to engineering pipelines for probabilistic molecular property prediction and Bayesian acquisition for active learning-based drug discovery. Additional activities may extend to include engineering pipelines for molecular generative modeling.
This is a 12-month contract based in South San Francisco, California.
Responsibilities
β’ Collaborate closely with scientists and develop machine learning and Bayesian optimization workflows to analyze existing and design new small and large molecules.
β’ Form close working relationships with small molecule and protein therapeutic development efforts.
β’ Work on existing projects and generate new project ideas.
Qualifications
β’ PhD in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS with 3+ years of industry experience.
β’ Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases).
β’ Record of achievement, including at least one high-impact first author publication or equivalent.
β’ Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit).
β’ Previous focus on one or more of the following:
β’ Molecular property prediction
β’ Computational chemistry
β’ De novo drug design
β’ Medicinal chemistry
β’ Small molecule design
β’ Self-supervised learning
β’ Geometric deep learning
β’ Bayesian optimization
β’ Probabilistic modeling
β’ Statistical methods
β’ Public portfolio of computational projects (e.g., GitHub).
Salary
β’ $97,801-$158,932
Why Choose R&D Partners?
As an employee, you have access to a comprehensive benefits package including:
β’ Medical insurance β PPO, HMO & HSA
β’ Dental & Vision insurance
β’ 401k plan
β’ Employee Assistance Program
β’ Long-term disability
β’ Weekly payroll
β’ Expense reimbursement
β’ Online timecard approval
R&D Partners is a global functional service provider and strategic staffing resource specializing in scientific, clinical research & engineering. We provide job opportunities within major pharmaceutical, biopharmaceutical, biotechnology, and medical device companies.
R&D Partners is an equal-opportunity employer.
Pharmaceutical company is looking for a talented Machine Learning Engineer to develop structural and machine learning-based methods for molecular design. The successful candidate will manage projects deploying new techniques for machine learning-based molecular optimization for the analysis and design of small and large molecule drugs within target-driven design campaigns. Special focus will be given to engineering pipelines for probabilistic molecular property prediction and Bayesian acquisition for active learning-based drug discovery. Additional activities may extend to include engineering pipelines for molecular generative modeling.
This is a 12-month contract based in South San Francisco, California.
Responsibilities
β’ Collaborate closely with scientists and develop machine learning and Bayesian optimization workflows to analyze existing and design new small and large molecules.
β’ Form close working relationships with small molecule and protein therapeutic development efforts.
β’ Work on existing projects and generate new project ideas.
Qualifications
β’ PhD in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS with 3+ years of industry experience.
β’ Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases).
β’ Record of achievement, including at least one high-impact first author publication or equivalent.
β’ Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit).
β’ Previous focus on one or more of the following:
β’ Molecular property prediction
β’ Computational chemistry
β’ De novo drug design
β’ Medicinal chemistry
β’ Small molecule design
β’ Self-supervised learning
β’ Geometric deep learning
β’ Bayesian optimization
β’ Probabilistic modeling
β’ Statistical methods
β’ Public portfolio of computational projects (e.g., GitHub).
Salary
β’ $97,801-$158,932
Why Choose R&D Partners?
As an employee, you have access to a comprehensive benefits package including:
β’ Medical insurance β PPO, HMO & HSA
β’ Dental & Vision insurance
β’ 401k plan
β’ Employee Assistance Program
β’ Long-term disability
β’ Weekly payroll
β’ Expense reimbursement
β’ Online timecard approval
R&D Partners is a global functional service provider and strategic staffing resource specializing in scientific, clinical research & engineering. We provide job opportunities within major pharmaceutical, biopharmaceutical, biotechnology, and medical device companies.
R&D Partners is an equal-opportunity employer.