Description:

• Machine Learning Engineer – Prescient Design.
• Hybrid Working Model.
• We are looking for talented Machine Learning Engineers to join Prescient Design, a division devoted to developing structural and machine learning-based methods for molecular design within Research and Early Development (gRED) organization.
• 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. 

For this role, we are seeking candidates with significant experience in at least one of the following areas, and ideally some experience in one or more of the others, listed in order of importance:

• Molecular property prediction
• Probabilistic modeling/inference
• Bayesian optimization or active learning
• Production software engineering or pipeline optimization
• Cheminformatics 

In addition to these skills, strong software engineering experience is required.

• 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.

The Role:

• Join Prescient Design within the Computational Sciences organization in gRED.
• Collaborate closely with scientists within Prescient and across gRED.
• Develop machine learning and Bayesian optimization workflows to analyze existing and design new small and large molecules.
• Form close working relationships with small molecule and protein therapeutic development efforts across gRED.
• Work on existing projects and generate new project ideas.

Qualifications:

• PhD in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics), or MS with 3+ years of industry experience.
• Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases).
• Record of achievement, including at least one high-impact first author publication or equivalent.
• Excellent written, visual, and oral communication and collaboration skills.

Additional Desired Qualifications:

• Experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit).

Previous focus on one or more of the following:

• Molecular property prediction.
• Computational chemistry.
• De novo drug design.
• Medicinal chemistry.
• Small molecule design.
• Self-supervised learning.
• Geometric deep learning.
• Bayesian optimization.
• Probabilistic modeling.
• Statistical methods.
• Public portfolio of computational projects (e.g., GitHub).

The hiring range for this position is $70 to $80 hour. The base pay actually offered will take into account internal equity, and may also vary depending on candidate's geographic region, job-related knowledge, skills, and experience amongst other factors

Harvest Technical Services is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or 

expression, sexual orientation, national origin, genetics, pregnancy, disability, age, veteran status, or any other federal, state, or local protected class.

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