Machine Learning Engineer
Role summary
PinSilico is seeking a creative and driven Machine Learning Engineer to advance its AI and physics-powered peptide discovery platform. This role is crucial for drug discovery, operating at the intersection of ML, computational chemistry, and scientific software engineering. You will develop and deploy generative and predictive ML models for peptide design, build property prediction models, and integrate ML into scientific pipelines. Responsibilities include developing scalable workflows for HPC environments, managing CI/CD pipelines, implementing advanced data strategies, and evaluating new AI methods. The ideal candidate has a Bachelor's degree or higher in a related field, 3+ years of industry experience, a deep understanding of modern ML architectures for molecular design, and fluency in Python with ML frameworks like PyTorch, JAX, or TensorFlow.
About the Role
We are seeking a creative and driven
ML Engineer
to help advance PinSilico—our AI and physics-powered peptide discovery platform. This role sits at the intersection of machine learning, computational chemistry, and scientific software engineering at the forefront of drug discovery. You will learn directly from seasoned drug hunters across chemistry, biology, and computation, gaining firsthand exposure to how new medicines are discovered and developed for patients. This is a high-impact, foundational hire shaping the core of our computational platform.
Responsibilities
- Develop and deploy generative and predictive machine learning models for peptide design, including sequence, structure, and property prediction
- Build and maintain property prediction models that track experiments and generate actionable insights
- Integrate ML models into existing cheminformatics and computational chemistry pipelines, with an emphasis on scalability and reproducibility
- Develop workflows that scale across heterogeneous HPC environments (on-premise and cloud)
- Build and maintain CI/CD pipelines for model testing and deployment
- Implement advanced data curation and splitting strategies that go beyond standard random sampling to prevent data leakage
- Track the latest developments in AI-based protein and molecular design, evaluating new methods for internal adoption
Qualifications
- Bachelor's degree or higher in Computer Science, Applied Mathematics, Computational Chemistry, or a related field, with 3+ years of industry experience developing ML applications
- Deep understanding of the mathematical foundations and architectures underlying modern ML models for molecular and protein design (e.g., VAEs, transformers, GNNs, diffusion models, equivariant networks)
- Experience with fine-tuning techniques to adapt large, pre-trained models for specialized tasks
- Fluency in Python and familiarity with ML frameworks used in scientific computing (e.g., PyTorch, JAX, or TensorFlow)
- Familiarity with cheminformatics and structural biology libraries (e.g., RDKit, DeepChem, Biopython, or equivalents)
- Excellent communication skills with the ability to convey complex computational concepts to scientists across disciplines
Preferred
- Experience in a fast-paced biotech or startup environment
- Familiarity with physics-based molecular simulations, including docking, MD, and free energy methods
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