onsite
Scientist II, Machine Learning - Guided Protein Design Evaluation - Profluent
ML Engineer
Lead the Machine Learning Design Evaluation program to develop and validate deep generative models for protein design, leveraging Python, PyTorch/TensorFlow, and advanced protein modeling techniques.
About the role
Key Responsibilities
- Design, implement, and scale deep generative models that generate novel protein sequences with desired functional properties.
- Develop evaluation pipelines that integrate computational predictions with experimental validation data.
- Collaborate with protein engineers, biochemists, and product teams to translate model outputs into actionable design hypotheses.
- Optimize model performance and reproducibility using modern ML frameworks (PyTorch, TensorFlow) and cloud infrastructure.
- Publish findings internally and externally, contributing to scientific literature and open‑source tools.
Requirements
- Ph.D. or equivalent experience in Machine Learning, Computational Biology, or a related field.
- Strong programming skills in Python and hands‑on experience with deep learning libraries such as PyTorch or TensorFlow.
- Demonstrated expertise in protein modeling, sequence analysis, or related bioinformatics methods.
- Proven ability to design end‑to‑end ML pipelines that incorporate experimental feedback loops.
- Excellent communication skills and a collaborative mindset for cross‑functional teamwork.
Skills
pythonpytorchtensorflowdeep learningmachine learning