Responsibilities
- Everything involved in applying a ML model to a production use case, including designing and coding up the neural network, gathering and refining data, training and tuning the model, deploying it at scale with high throughput and uptime, and analyzing the results in the wild in order to continuously update and improve accuracy and speed
- Write and maintain scalable, performant code that can be shared across platforms
- Contribute meaningfully to the product and core backend systems by suggesting and executing improvements
- Improve engineering standards, tooling, and processes
- Develop novel, accurate, and performant ML algorithms for use at scale
- Conduct metric-driven research experiments to improve model performance
- Provide mentorship to and help onboard ML engineers
- Lead cross-functional collaboration with other teams
- Contribute to defining strategic direction, planning the roadmap
- Maintain awareness of industry best practices for data maintenance handling as it relates to your role
- Adhere to policies, guidelines and procedures pertaining to the protection of information assets
- Report actual or suspected security and/or policy violations/breaches to an appropriate authority