Senior Machine Learning Engineer

Summary

Join Flip.shop, a rapidly growing social commerce company, as a Senior Machine Learning Engineer specializing in Machine Learning Infrastructure. You will design, build, and optimize the infrastructure powering our AI-driven platform. This role involves ensuring efficient deployment, scaling, and monitoring of machine learning models, streamlining the development lifecycle, and creating scalable, production-level systems. You will collaborate with data scientists, machine learning engineers, and DevOps teams. The position requires a Bachelor's degree in a related field, 3+ years of experience, and proficiency in various technical skills. Flip.shop offers a competitive compensation and benefits package.

Requirements

• Bachelor's degree or higher in Computer Science or a related field, with 3+ years of experience in building scalable systems
• 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
• 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
• Experience in using or designing open-source machine learning lifecycle management systems like TFX
• 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

Responsibilities

• 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
• Optimize the serving and training infrastructure of machine learning models
• Enhance the workflow for model training and serving, data pipelines, storage systems, and resource management within multi-tenancy machine learning systems
• Build tools to automate workflows for model training, testing, and deployment, ensuring that machine learning models can move quickly from development to production
• Ensure the infrastructure supports high-performance model inference at scale, with a focus on minimizing latency and maximizing throughput
• Work closely with data scientists, machine learning engineers, and DevOps teams to create seamless integration between development and production environments
• Build robust monitoring systems to track model performance and infrastructure health, ensuring reliability and uptime of machine learning services
• Implement best practices in infrastructure security, data privacy, and compliance, particularly when handling sensitive user data

Benefits

• Equity
• Bonuses
• Long term incentives
• A PTO policy
• Other progressive benefits