Product Team-Embedded ML Engineer

Context:
- They can integrate machine learning models and techniques into the products and systems developed by a specific software team.
- They can work closely with other members of the software team to ensure that the machine learning functionality is properly integrated into the overall system.
- They can troubleshoot and debug.
- ...
Example(s):
- one embedded into a Clinical Study Participant App Team.
- ...
Counter-Example(s):
- one within Personalized Recommender Service Team.
- ...
See: Embedded Backend Engineer.

References

chat
- Q: An embedded ML Engineer is an ML engineer who works within a specific software team rather than within a separate ML team. Describe the role of an embedded ML Engineer.
- A: An embedded ML engineer is responsible for integrating machine learning models and techniques into the products and systems developed by a specific software team. This may include tasks such as selecting appropriate models, optimizing model performance, and implementing models in production. Additionally, an embedded ML engineer may work closely with other members of the software team to ensure that the machine learning functionality is properly integrated into the overall system, and may also be involved in troubleshooting and debugging as needed. Overall, the role of an embedded ML engineer is to apply machine learning expertise to enhance the functionality and performance of the team's products and systems.