Senior ML Engineer (AI Research/ Portability)
Tasks
- Build AI agent architectures
- Build APIs and integration layers
- Build multi-agent workflows
- Build retrieval systems
- Collaborate cross-functional teams
- Create evaluation frameworks
- Define agent schemas and interfaces
- Design communication protocols
- Design machine learning models
- Develop benchmarks
- Develop model routing systems
- Develop research software
- Document research findings
- Evaluate machine learning models
- Implement memory and context management
- Implement testing infrastructure
- Optimize quality cost latency
- Prototype agent interoperability
- Research optimization techniques
- Train machine learning models
Perks/Benefits
- Career growth
- Continuous learning support
- Flexible work environment
- International collaboration
- Ownership and autonomy
Skills/Tech-stack
AI Agents | APIs | Agent systems | CI/CD | Data schemas | Distillation | Distributed Systems | Evaluation | Experimentation | Generalization | Human Feedback | Knowledge graphs | Language Models | Large Language Models | Learning from Human Feedback | Machine Learning | Memory Architecture | Model routing | Multi-Agent | Multi-Agent Systems | Observability | Preference Learning | Privacy | Provenance | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reproducibility | Retrieval-Augmented Generation | Reward Modeling | Security | Statistical Analysis | Testing | Uncertainty Estimation | Vector Databases | Version control
Education
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