Senior Machine Learning Engineer, Agentic Science/Generative Models, AI for Biology & Translation (AIBT)
South San Francisco, United States
USD 168K-312K Senior-level Full Time
Tasks
- Build agent memory and context management for long horizon workflows
- Build agents for multi step scientific workflows
- Build finetune deploy and scale foundation models and LLMs in production
- Design evaluation harnesses against scientific ground truth
- Implement self improving autonomous loops for autoML and lab in the loop
- Interface agents with biological genomic clinical data sources
- Manage MLOps and AgentOps lifecycle experiment tracking evaluation monitoring reproducibility CI CD
- Optimize inference time compute sampling and search strategies model routing batching caching
- Own production Python PyTorch or JAX codebases
- Use infrastructure as code for reliable scalable deployments
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS SageMaker | Agent Orchestration | AgentOps | Agentic Systems | Amazon SageMaker | Automated testing | Automl | Batching | CI/CD | Caching | Distributed Training | EC2 | EKS | Experiment tracking | Foundation Models | Git | Helm | Infrastructure as Code | JAX | Kubernetes | Langgraph | Language Models | Large Language Models | Latency optimization | MLOps | Machine Learning | Model Evaluation | Model routing | Monitoring | PyTorch | Python | Reproducibility | S3 | Sampling | Search | Terraform | “as-code”
Education
Regions
Countries
States
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