Senior Machine Learning Engineer, Trust
Tasks
- Add guardrails for agent quality
- Build end-to-end ML pipelines
- Conduct experiments and holdouts
- Create benchmarks and evaluation harnesses
- Deploy ML models batch and real time
- Design and launch AI agents for trust decisions
- Detect abuse behavior
- Frame ML problems
- Generalize detection across defenses
- Implement agent orchestration and tool interfaces
- Improve models with structured and unstructured data
- Instrument model and agent metrics
- Perform code reviews and ship testable code
- Prototype ML solutions
- Quantify impact with business metrics
- Train and evaluate ML models
- Use LLMs to accelerate model development
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Account integrity | Agentic Frameworks | Anomaly Detection | B testing | Batch Processing | Boosted Trees | Calibration | Data Engineering | Deep learning | Deployment | Document models | Evaluation methodology | Feature Engineering | Fraud Detection | Generative AI | Gradient Boosted Trees | Ground Truth | Identity verification | Java | LLM | Machine Learning | Model Deployment | Model Evaluation | Model Training | Multimodal Models | Neural Networks | Offline metrics | Online metrics | Orchestration | PyTorch | Python | Real Time | Real-time Systems | Scala | Speech Models | TensorFlow | Test-Driven | Test-Driven Development | Time Systems | Transformer | Vision Models
Education
Regions
Countries
States
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