about 5 hours ago
Responsibilities
- Build and maintain scalable machine learning solutions in production.
- Train and validate deep learning and statistical models based on use-case and performance.
- Develop a deep understanding of the applications and the rationale behind models and systems.
- Collaborate with product managers and stakeholders to analyze business problems and define system scope.
- Work with data platform teams to create robust batch and real-time data pipelines.
- Enhance productivity by building tools and maintaining ML models with software engineers.
- Mentor team members and uphold high engineering standards.
- Implement engineering best practices such as code reviews and automated testing.
Requirements
- 7+ years of applied machine learning experience with proficiency in Python.
- Strong foundation in machine learning and deep learning principles.
- Experience building and maintaining ML models in production environments.
- Ability to design and architect large-scale experiments to inform product roadmaps.
- Familiarity with ML frameworks like PyTorch, TensorFlow, or Keras.
- Knowledge of ML Ops concepts for testing and maintaining models in production.
- Experience with big data tools and components like Kafka, Apache Spark, and DynamoDB.
- Experience working in an agile team environment with changing priorities.
- Experience with AWS.
Benefits
- Competitive pay and generous time off.
- Ample parental and wellness leave.
- Healthcare and retirement savings program.
- Support for volunteering and donation efforts.
Tech Stack
Categories
AI & MLData Engineering