About the Role
The role involves building and maintaining AI-driven features that enhance document understanding and user recommendations, with a focus on scalable and reliable systems.
Responsibilities
- Develop machine learning models for document analysis and content extraction
- Implement and optimize natural language processing pipelines
- Collaborate on recommendation systems using behavioral data
- Design scalable APIs to serve AI models in production
- Monitor model performance and iterate based on feedback
- Work closely with product teams to define AI use cases
- Improve data quality and labeling processes for training sets
- Integrate third-party AI tools where appropriate
- Maintain documentation for models and training workflows
- Ensure models comply with data privacy standards
- Optimize inference speed and resource usage
- Troubleshoot issues in live AI systems
- Contribute to version control and code review processes
- Participate in sprint planning and technical reviews
- Support deployment and monitoring of ML infrastructure
- Evaluate new AI frameworks and libraries
- Refine model accuracy through experimentation
- Assist in defining success metrics for AI features
- Work with structured and unstructured data sources
- Build automated testing for model outputs
- Collaborate on user-facing features powered by AI
- Ensure reproducibility of training runs
- Manage model versioning and deployment lifecycle
- Contribute to technical architecture discussions
- Respond to performance alerts in production systems
Nice to Have
- Advanced degree in computer science or related field
- Published research in machine learning or NLP
- Experience with large-scale document processing
- Contributions to open-source ML projects
- Prior work on recommendation engines
- Experience with retrieval-augmented generation systems
- Knowledge of prompt engineering techniques
- Background in search algorithms
- Familiarity with legal or publishing domains
- Experience mentoring junior engineers
Compensation
Competitive salary with equity package
Work Arrangement
Remote
Team
Small, cross-functional product and engineering team
Tech Stack
Python, PyTorch, FastAPI, Docker, AWS, PostgreSQL, Redis, Git, Kubernetes
Application Process
- Submit your resume and a brief explanation of a machine learning project you're proud of
- Complete a take-home coding challenge focused on model design
- Participate in a technical interview with the engineering team
- Final interview with product and engineering leads
Not offered
