Machine Learning Engineer
- cerebra-consulting
164 Days Ago
NA
NA
Remote
7-12 Years
Required Skills: ensorFlow, PyTorch, scikit-learn
Job Description
- Kroger (84.51) Data Scientists are using Databricks to train machine learning models for search functions on websites.
- Once a particular Machine Learning Model is ready, they will hand of to the Machine Learning Engineer, who will put the model into production environment (support, monitoring, etc).
- The Machine Learning Engineer will be “fine tuning the model in production” – ensure it is learning properly, making sure it is evolving correctly and maintaining and monitoring the various models.
- This is the work a normal “Data Scientist” doesn’t want to do – they traditional Data Scientist wants to go play around with new models and the Machine Learning Engineer wants to be ensure the models in production are cultivated properly and get the support they need to become best versions of themselves.
- Data scientists expect a lot from Machine Learning Engineers:
- Programming skills (Python, R).
- Expect this person to be able to take initiative.
- Know how to fine tune to a Kroger environment
Key Responsibilities
Diverse ML Platform Expertise:
• Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, but open to other systems as needed.
• Leverage support for open-source frameworks like TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks via custom containers.
• Stay updated with the latest trends in MLOps and ML technologies.
Recommender System Design and Development:
• Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, transformers, and LLMs.
• Software engineering skills to work with teams integrating the recommender systems into customer facing products.
• Experience in AB testing and iterative optimization using data driven approaches.
• Understanding of infrastructure needs required to deploy ML systems (CPU/GPU, networking infrastructure).
