About the Role
Develop and refine machine learning systems focused on large language models, contributing to model architecture, training pipelines, and real-world deployment strategies.
Responsibilities
- Design scalable training pipelines for large language models
- Optimize model inference speed and resource usage
- Collaborate with researchers to implement novel architectures
- Evaluate model performance using quantitative benchmarks
- Improve model alignment with human intent
- Debug and resolve issues in distributed training environments
- Integrate models into production-grade applications
- Monitor model behavior for anomalies and biases
- Contribute to data curation and preprocessing workflows
- Support deployment of models across multiple platforms
- Maintain documentation for model versions and experiments
- Work with engineering teams to ensure system reliability
- Assist in defining evaluation metrics for model outputs
- Implement safety and moderation protocols for model responses
- Stay current with advancements in natural language processing
- Participate in code reviews and technical design discussions
- Refactor legacy systems to support new model capabilities
- Ensure compliance with data privacy standards
- Collaborate on open-source contributions when applicable
- Troubleshoot model drift and performance degradation
- Assist in scaling infrastructure for larger model training
- Develop tools for automated model testing and validation
- Contribute to internal research publications
- Engage in cross-functional planning sessions
- Support reproducibility of machine learning experiments
Nice to Have
- Contributions to open-source machine learning projects
- Experience with model quantization techniques
- Familiarity with prompt engineering strategies
- Background in multilingual language models
- Knowledge of model interpretability methods
- Experience with low-rank adaptation techniques
- Understanding of retrieval-augmented generation
- Participation in AI research competitions
- Prior work with sparse models
- Familiarity with federated learning concepts
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid remote setup with flexible hours
Team
Collaborative AI research and development team
Research Focus
- Focus on advancing capabilities of large language models through experimentation and iteration
- Collaborate on publishing findings in academic or industry venues
Technology Stack
- Primary use of PyTorch for model development
- Infrastructure built on Kubernetes and cloud services
- Heavy reliance on Git for version control
Performance Expectations
- Deliver measurable improvements in model efficiency
- Maintain high standards for code quality and testing
Available for qualified candidates
