Director, Discovery Bioinformatics Oncology
USD 193K-283K Executive-level Full Time
Tasks
- Architect and operationalize machine learning models
- Build target identification and validation models
- Conduct omics analysis with QC and statistics
- Create knowledge retrieval and LLM workflows
- Design and oversee computational and experimental validation
- Develop protein and antibody sequence design models
- Establish model governance and validation SOPs
- Implement MLOps for training evaluation monitoring
- Implement multimodal data integration pipelines
- Lead AI ML strategy for discovery
- Mentor and lead cross functional ML and data teams
- Operationalize active learning design make test loops
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | AWS | Active Learning | Adapters | Agentic Workflows | Antibody Design | Batch | CI/CD | CRISPR Screen Analysis | Causal Inference | Deep learning | Diffusers | Diffusion Models | Docker | EKS | Experiment tracking | Experimental Design | FAIR-Data | Feature Engineering | Feature Stores | Git | GitHub | Graph Learning | Graph Neural Networks | Hugging Face | Hugging Face Transformers | JAX | Kubernetes | Lambda | Language Models | Large Language Models | LoRA | MLOps | Machine Learning | Model Registry | Multimodal Learning | Neural Networks | Ontology Harmonization | PEFT | Prompt engineering | Protein Design | Protein Structure Prediction | Protein structure | Proteomics | PyTorch | Python | RNA Sequencing | Retrieval-Augmented Generation | S3 | SageMaker | Screen analysis | Single-cell | Single-cell RNA-sequencing | Statistical Analysis | Step Functions | Structure prediction | TensorFlow | Transformers | Vector Databases | WES | WGS
Education
Roles
AI | AI Engineer | Director | Engineer | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
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