Principal Scientist, Machine Learning, Biomolecules
Tasks
- Contribute to project planning budgets and timelines
- Design LLM based agent and workflow systems
- Drive benchmarking scaling and production implementation
- Lead development and delivery of AI ML or computational projects
- Manage and mentor cross functional scientists and engineers
- Oversee method development and pipeline development
- Present technical findings to diverse audiences
- Promote best practices in AI development
- Represent company to partners and portfolio teams
- Scout emerging literature and synthesize strategies
Perks/Benefits
Skills/Tech-stack
3D structure modeling | ADMET | AWS Batch | AWS ECS | AWS EKS | AWS S3 | AWS SageMaker | Agentic Workflows | Airflow | Amazon Redshift | Argo | CI/CD | Cheminformatics | DVC | Data Lineage | Data Warehousing | Databases | Deep learning | Diffusion Models | Docker | Docking | Faiss | FastAPI | Flow Models | Genomics | GitHub Actions | Gradio | Graph Neural Networks | Infrastructure as Code | JAX | LakeFS | Language Models | Large Language Models | MLflow | Machine Learning | Mass Spectrometry | Molecular Modeling | Neural Networks | PGVector | Pose quality metrics | Prefect | Protein Design | PyTorch | Python | Quality metrics | Snowflake | Streamlit | Structure modeling | Systems Biology | TensorFlow | Terraform | Variational Autoencoders | Vector Search | Version control | Weights and Biases | Workflow Orchestration | “as-code”
Education
Regions
Countries
States
Cities
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