Director, Discovery Bioinformatics Oncology
US: South San Francisco Haskins, United States
USD 193K-338K Executive-level Full Time
Tasks
- Architect deploy machine learning models for target identification validation
- Build data integration platforms for multimodal datasets
- Conduct omics analysis with FAIR practices
- Design and oversee computational and experimental benchmarking
- Develop retrieval augmented and agentic LLM workflows
- Harmonize ontologies and create model ready feature stores
- Implement active learning design make test loops
- Integrate ML predictions with experimental assays
- Lead AI ML strategy for oncology discovery
- Manage model governance and experiment tracking
- Mentor and grow ML bioinformatics teams
- Operationalize computational protein and antibody design
- Own MLOps for training evaluation monitoring
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS | Active Learning | Adapters | CI/CD | CRISPR screens | Causal Inference | Computer Vision | DNA-Seq | Diffusion Models | Docker | Docker Compose | EKS | Experiment tracking | Experimental Design | Feature Stores | Foundation Models | Git | GitHub | Graph Learning | Hugging Face | Hydra | Imaging | JAX | Knowledge graphs | Kubernetes | LLM | Lambda | LoRA | MLOps | MLflow | Model Governance | Model Registry | Ontology Harmonization | PEFT | Prompt engineering | Proteomics | PyTorch | PyTorch Lightning | Python | RAG | RNA-seq | Retrieval-Augmented Generation | S3 | SageMaker | Single-cell | Single-cell Transcriptomics | Statistical Analysis | Step Functions | TensorFlow | Transformers | Vector Databases | Weights and Biases
Education
Regions
Countries
States
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