Senior ML Engineer (AI Research/ Portability)
Tasks
- Build AI agent architectures
- Build memory and context management systems
- Build quality cost latency optimization
- Build research software
- Collaborate with research and engineering teams
- Create evaluation frameworks and benchmarks
- Create testing infrastructure
- Define agent schemas interfaces and standards
- Design machine learning models
- Develop APIs and integration layers
- Develop model routing systems
- Develop tool execution and multi agent workflows
- Document technical findings
- Evaluate machine learning models
- Implement machine learning models
- Implement retrieval systems
- Prototype agent interoperability solutions
- Research optimization techniques
- Train machine learning models
Perks/Benefits
- Career growth opportunities
- Collaborative culture
- Continuous learning support
- Flexible work environment
- Inclusive workplace
- Innovation focus
- International team collaboration
- Ownership and autonomy
Skills/Tech-stack
AI Agents | APIs | Artificial Intelligence | Benchmarks | CI/CD | Data schemas | Distillation | Distributed Systems | Evaluation Frameworks | Generalization | Interoperability | Interprocess Communication | Knowledge graphs | Language Models | Large Language Models | Machine Learning | Memory Architectures | Mixture of Experts | Model routing | Observability | Privacy | Provenance | Python | Recommender Systems | Reinforcement Learning | Reproducibility | Retrieval systems | Reward Modeling | Security | Testing | Uncertainty Estimation | Vector Databases | Vector Search | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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