Machine Learning Operations Engineer

Summary

Join Simbe Robotics and become a Machine Learning Operations Engineer, supporting our cutting-edge AI and robotics technologies that transform retail operations. You will maintain and manage machine learning infrastructure, ensuring seamless model training, optimization, and deployment. This role requires expertise in Linux server maintenance, scripting, neural network training, and model optimization. Experience with Google Cloud Platform and specialized hardware is preferred. The ideal candidate is a computer enthusiast with a passion for machine learning and collaborative work. Simbe offers a competitive salary and a comprehensive benefits package, details of which will be provided upon offer of employment. Be a part of a dynamic, inclusive team reshaping the future of retail.

Requirements

• Proven experience with Linux server maintenance, including both on-premises and cloud environments
• Proficient in scripting with Bash and Python to streamline system and model management
• Hands-on experience with neural network training, data loaders, and data pre-processing pipelines
• Familiar with data and model parallelism strategies for improving training speed and efficiency
• Knowledgeable in neural network model conversion and optimization for deployment on diverse hardware

Responsibilities

• Maintain and manage the software configuration of on-premises machine learning hardware to support optimal performance for training neural networks
• Set up and maintain cloud-based training environments, primarily on Google Cloud Platform, to facilitate model experimentation and scalability
• Automate training workflows to drive continuous improvement of vision models, reducing manual overhead and enhancing efficiency
• Develop automated accuracy assessments and generate reports to evaluate and compare the performance of newly trained neural networks against existing models
• Ensure predictable and efficient turnaround times for training models with updated datasets to meet project timelines
• Organize and manage model weights and associated documentation in various formats for deployment across on-premises, cloud, and edge environments
• Apply quantization and pruning techniques to models to enhance computational efficiency without sacrificing accuracy
• Design and deploy infrastructure for low-latency inference to enable real-time performance for large-scale models (e.g., vLLMs)

Preferred Qualifications

• Familiarity with Google Cloud Platform for machine learning operations
• Experience with specialized hardware platforms such as Nvidia Jetson, Triton Inference Server, and NIM
• Skilled in OpenVINO and ONNX for model conversion and optimization
• Experience training or fine-tuning large language models (LLMs) would be a significant advantage
• Programming experience in Python and C++ is beneficial but not mandatory
• Strong written and verbal communication skills for documentation and collaboration
• Passion for machine learning technology and an aptitude for problem-solving in fast-paced environments

Benefits

• Equity compensation
• Full range of medical, financial, and/or other benefits