Software Engineer - Machine Learning Infrastructure
July 31
🏡 Remote – New York
Description
• Developing Scalable ML Infrastructure: You will assist in designing and developing scalable, efficient, and robust machine learning infrastructure that supports the lifecycle of ML models—from data ingestion and processing to training, evaluation, and deployment. You will contribute to ensuring this infrastructure can handle large-scale data and complex computations with high reliability and performance. • Improving ML Operations (MLOps): You will innovate and implement best practices in MLOps to streamline the development, deployment, and monitoring of ML models. This includes automating pipelines, ensuring model reproducibility, and facilitating seamless model versioning and rollout. • Collaborating with Cross-Functional Teams: You will work closely with product managers, data scientists, and other engineering teams to understand their needs and help translate them into ML platform capabilities. Your collaborative efforts will ensure that the ML infrastructure effectively supports product features and business objectives. • Technical Guidance and Support: You will provide guidance and support to junior engineers and peers, sharing your knowledge and expertise to promote a culture of technical excellence. Your contributions will help develop the team's skills and foster innovation. • Staying Updated with Industry Trends: You will stay informed about the latest developments in machine learning, data engineering, and cloud technologies. By keeping up with industry trends and challenges, you will help ensure that the ML platform remains up-to-date and competitive.
Requirements
• 3+ years of Software Engineering Experience • 1+ years with Machine learning or developing machine learning platforms • Strong communication and architecture skills • Python experience and k8s experience
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