Senior Machine Learning Engineer - Machine Learning Infrastructure

Responsibilities:

• Infrastructure Development: Design and implement scalable infrastructure for deploying, monitoring, and maintaining machine learning models in production environments. Design and implement machine learning systems for feeds, ads, and search ranking models.
• Training Infrastructure: Optimize the serving and training infrastructure of machine learning models.
• Model Training: Enhance the workflow for model training and serving, data pipelines, storage systems, and resource management within multi-tenancy machine learning systems.
• Tooling & Automation: Build tools to automate workflows for model training, testing, and deployment, ensuring that machine learning models can move quickly from development to production.
• Performance Optimization: Ensure the infrastructure supports high-performance model inference at scale, with a focus on minimizing latency and maximizing throughput.
• Collaboration: Work closely with data scientists, machine learning engineers, and DevOps teams to create seamless integration between development and production environments.
• Monitoring & Maintenance: Build robust monitoring systems to track model performance and infrastructure health, ensuring reliability and uptime of machine learning services.
• Security & Compliance: Implement best practices in infrastructure security, data privacy, and compliance, particularly when handling sensitive user data.

Requirements:

• Education: Bachelor's degree or higher in Computer Science or a related field, with 3+ years of experience in building scalable systems.
• Technical Skills: Proficiency in one or two programming languages (C/C++, Golang) within a Linux environment.
• Solid understanding of GPU hardware architecture, GPU software stack (CUDA, cuDNN), and experience in GPU performance analysis.
• Experience in deep model inference/training, debugging, and tuning.
• ML Workflow Knowledge: Familiarity with mainstream machine learning frameworks (e.g., TensorFlow, PyTorch, MxNet).
• Familiarity with MLOps practices.
• Experience with big data frameworks (e.g., Spark, Hadoop, Flink) and resource management and task scheduling for large-scale distributed systems.
• Open-source: Experience in using or designing open-source machine learning lifecycle management systems like TFX.

Key Skills

• Excellent logical analysis and problem-solving skills with the ability to abstract and decompose complex business logic.
• Strong sense of responsibility, good learning ability, communication skills, and self-motivation, with the ability to respond and act quickly.
• Good working document habits, with timely writing and updating of workflow and technical documentation.