Job Description:

• Partner with ML and Computational Chemistry researchers to translate internal scientific code and prototypes into robust, production-ready software systems for external product offerings.
• Implement and integrate core scientific algorithms (ML, deep learning, physics-based simulation) into scalable product platforms.
• Collaborate within a multi-disciplinary "pod" of engineers, computational chemists, AI experts, and product managers to define requirements, architecture, and deployment strategies for new features.
• Ensure the scientific integrity and accuracy of research models (e.g., active learning, FEP, etc.) when deployed in production.
• Drive the product lifecycle from scientific inception to external deployment, ensuring technical excellence and market fit.

Requirements:

• PhD in chemistry, biology, or a related discipline.
• 3-5 years of relevant experience in a product-focused engineering or applied science role, specifically translating ML or computational chemistry research into production software in the private sector (biotech/pharma preferred).
• Experience deploying and maintaining machine learning models for scientific applications (e.g., molecular property prediction, multi-objective optimization).
• Proficiency with Python toolkits for scientific computing (e.g., NumPy, Pandas, SciPy) and machine learning (e.g., scikit-learn, PyTorch).
• Strong communication skills and a drive to collaborate with colleagues to identify problems and communicate technical solutions in an accessible manner.

Benefits:

• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
• Retirement savings with company matching
• Paid parental leave
• Inclusive family-building benefits
• Fully remote
• Flexible paid time off
• Company-wide seasonal breaks
• Support for flexible work arrangements that enable sustainable performance
• Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs