Machine Learning Engineer (Contract)
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Job Description
About the Company:
The company is seeking a Machine Learning Engineer to join a high-impact ML Operations team focused on applied AI. They emphasize bringing machine learning models from experimentation to real-world, production-ready applications. The team supports a wide range of cutting-edge R&D initiatives, focusing on model optimization for deployment across various hardware (cloud and embedded systems).
Job Description:
This role involves designing and building scalable machine learning models for production environments. Specific responsibilities include:
• Model Development and Deployment: Designing and building scalable machine learning models specifically for production environments. This includes creating efficient training pipelines for large datasets (image and text) and optimizing model performance across different hardware (CPU, GPU, and embedded systems). Deployment to real-time cloud and edge systems is a key component.
• Pipeline Creation and Optimization: Creating and implementing data preprocessing and feature engineering pipelines. The engineer will be responsible for optimizing model performance and ensuring efficient training processes for large datasets.
• Technology Stack Proficiency: The role requires hands-on experience with tools like Databricks, MLflow, Docker, and AWS (SageMaker, EC2, S3). Strong SQL skills are also essential.
• Collaboration and Communication: Collaboration with cross-functional teams is crucial for successful deployment of ML solutions. Excellent communication skills are a must.
• MLOps and Best Practices: Staying current with the latest best practices and technologies in machine learning and MLOps is expected. A test-driven software development mindset is highly valued.
• Embedded Systems (Bonus): Experience optimizing ML models for embedded systems is considered a bonus.
The role requires 4+ years of professional experience delivering ML solutions into production, with proficiency in Python and Spark (hands-on experience is mandatory). Experience with ML frameworks such as TensorFlow and PyTorch is also required. A solid understanding of cloud-based ML workflows and tools is essential.
