Lead Machine Learning Engineer, Infrastructure

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Requirements:

• Passion for ML Infrastructure: We value enthusiasm for advancing ML infrastructure.
• Proven Impact: Show us your track record of delivering impactful solutions.
• Innovative Thinker: Bring creativity and fresh ideas to the table.
• Technical Proficiency: Solid foundation in software engineering and ML concepts.
• Collaborative Mindset: Strong communication and teamwork skills are a must.
• Continuous Learner: Stay updated with the latest advancements in the field.
• Problem-Solving Skills: Ability to tackle complex problems effectively.
• Adaptability: Thrive in a fast-paced, dynamic environment.

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 to 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 the latest advancements in machine learning infrastructure, distributed computing, and cloud technologies, and integrate 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.