Machine Learning Operations Engineer
Description For Machine Learning Operations Engineer
Simbe Robotics is seeking a Machine Learning Operations Engineer to join their innovative team working at the intersection of retail, AI, and robotics. This role focuses on supporting machine learning infrastructure, ensuring seamless model training, optimization, and deployment. The ideal candidate will manage both on-premises and cloud-based ML systems, with a particular emphasis on Google Cloud Platform.
The position offers a comprehensive compensation package ranging from $90,000 to $150,000, with additional benefits and potential equity compensation. This is a remote position, allowing for flexibility while working with cutting-edge technologies.
Key responsibilities include maintaining ML hardware configurations, automating training workflows, optimizing model performance, and ensuring efficient deployment across various environments. The role requires expertise in Linux server maintenance, Python scripting, and neural network training, with a strong foundation in ML infrastructure management.
Simbe's culture is built on their R.E.T.A.I.L. values: Result Driven, Empathetic, Transparent, Agile, Innovative, and Leaders. They foster a dynamic, inclusive environment where team members can contribute to transforming retail operations through advanced technology solutions.
The ideal candidate will combine technical expertise with strong communication skills, thriving in a fast-paced environment while collaborating with cross-functional teams. This role offers an exciting opportunity to work with cutting-edge AI and robotics technologies while making a significant impact on the future of retail operations.
Responsibilities For Machine Learning Operations Engineer
- Maintain and manage on-premises machine learning hardware for optimal neural network training performance
- Set up and maintain cloud-based training environments on Google Cloud Platform
- Automate training workflows for vision models improvement
- Develop automated accuracy assessments and generate performance reports
- Ensure efficient turnaround times for model training
- Organize and manage model weights and documentation across various environments
- Apply quantization and pruning techniques to optimize models
- Design and deploy infrastructure for low-latency inference
Requirements For Machine Learning Operations Engineer
- Experience with Linux server maintenance in on-premises and cloud environments
- Proficiency in Bash and Python scripting
- Hands-on experience with neural network training and data processing
- Knowledge of data and model parallelism strategies
- Understanding of neural network model conversion and optimization
- Strong written and verbal communication skills
- Experience with Google Cloud Platform for ML operations
