About the Role
Design and implement machine learning models and NLP pipelines to enhance the platform's ability to understand and process human language at scale.
Responsibilities
- Develop and optimize natural language processing models for production environments
- Collaborate with product teams to integrate AI features into user-facing applications
- Evaluate model performance using statistical and qualitative methods
- Refine data pipelines to support training and inference workflows
- Stay current with advancements in NLP and transformer-based architectures
- Contribute to the design of annotation schemas for training data
- Improve model accuracy through iterative experimentation
- Monitor system behavior in production for degradation or bias
- Write clean, maintainable code with thorough documentation
- Troubleshoot issues across the machine learning lifecycle
- Work with large, unstructured text datasets
- Apply domain adaptation techniques to improve model generalization
- Support deployment of models using containerized services
- Participate in code and model design reviews
- Ensure compliance with data privacy and security standards
- Optimize inference latency and resource usage
- Assist in scaling data labeling operations
- Contribute to version control and CI/CD practices
- Help define metrics for model success
- Collaborate on error analysis to guide future development
- Use Python and related data science libraries for prototyping
- Work with cloud infrastructure providers for compute needs
- Implement testing frameworks for model validation
- Support reproducibility in training and evaluation
- Engage in technical planning and sprint execution
Nice to Have
- Master's or PhD in computational linguistics or machine learning
- Published research in NLP or related conferences
- Experience with multilingual NLP systems
- Knowledge of prompt engineering and fine-tuning LLMs
- Familiarity with retrieval-augmented generation
- Experience with model quantization or distillation
- Background in low-resource language modeling
- Contributions to open-source NLP projects
- Understanding of model explainability methods
- Experience with real-time inference systems
- Work with speech-to-text or text-to-speech pipelines
- Knowledge of regulatory frameworks for AI
- Experience in startup environments
- Ability to mentor junior engineers
- Project leadership in machine learning initiatives
Compensation
Competitive salary and equity package
Work Arrangement
Remote-first with flexible hours
Team
Small, cross-functional team focused on rapid iteration
Our Approach to AI Ethics
- We prioritize responsible development practices in all model design decisions
- Team members are expected to consider fairness, transparency, and accountability
Growth Opportunities
- Engineers lead initiatives based on interest and impact
- Opportunities to shape technical direction and mentor others
Available for qualified candidates
