Lead Machine Learning Infrastructure Engineer - Infrastructure & Data

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Requirements:

• Strong technical expertise in designing and building scalable ML infrastructure.
• Experience with distributed systems and cloud-based ML platforms.
• Proficiency in programming languages such as Python, Java, or Scala.
• Deep understanding of ML workflows, including data pipelines, model training, and deployment.
• Passion for innovation and eagerness to implement the latest advancements in ML infrastructure.
• Strong problem-solving skills and ability to optimize complex systems for performance and reliability.
• Collaborative mindset with excellent communication skills to work across teams.
• Ability to thrive in a fast-paced, dynamic environment with evolving technical challenges.

Responsibilities:

• Design, implement, and optimize distributed systems and infrastructure components to support large-scale machine learning workflows, including data ingestion, feature engineering, model training, and serving.
• Develop and maintain frameworks, libraries, and tools that streamline the end-to-end machine learning lifecycle, from data preparation and experimentation to model deployment and monitoring.
• Architect and implement highly available, fault-tolerant, and secure systems that meet the performance and scalability requirements of production machine learning workloads.
• Collaborate with machine learning researchers and data scientists to understand their requirements and translate them into scalable and efficient software solutions.
• Stay current with advancements in machine learning infrastructure, distributed computing, and cloud technologies, integrating them into our platform to drive innovation.
• Mentor junior engineers, conduct code reviews, and uphold engineering best practices to ensure the delivery of high-quality software solutions.