Mid-level/Senior Machine Learning Engineer

Summary

Join Goldbelly as a Machine Learning Engineer and enhance how millions of customers connect with food experiences on our platform. Collaborate with business leaders and leverage cutting-edge machine learning and engineering resources to transform user experience. You will design, develop, and maintain robust, scalable machine learning systems for personalized food experiences. Develop algorithms using state-of-the-art techniques in search, retrieval, recommendation, and NLP. Improve core machine learning infrastructure, focusing on product and user embeddings. Apply software engineering best practices to machine learning, including CI/CD and pipeline orchestration. Define and promote best practices throughout the machine learning life cycle.

Requirements

• 1+ years of experience in ML modeling and working on large scale production systems
• Proficient in Python and SQL
• Experienced with data processing libraries such as Pandas and NumPy
• Skilled in machine learning libraries and frameworks including Scikit-learn, PyTorch, TensorFlow, or Keras
• Proficient in using version control systems like Git and collaborative development platforms such as GitHub or GitLab
• Strong background in developing personalized search and recommendation systems, preferably in an e-commerce context
• Familiarity with development in containerization and cloud computing environments (we use AWS, but experience in other platform is useful as well)

Responsibilities

• Collaborate closely with senior engineers, data scientists / analysts, and product managers to improve our search, recommendations, and personalization algorithms
• Design, develop, and maintain robust, scalable machine learning systems to ensure the seamless delivery of personalized food experiences
• Develop algorithms utilizing state-of-the-art machine learning techniques in search, retrieval, recommendation, and natural language processing (NLP)
• Improve our core machine learning infrastructure, focusing on product and user embeddings to boost efficiency and effectiveness across all ML-driven services
• Apply software engineering rigor and best practices to machine learning, including CI/CD, pipeline orchestration, etc
• Define and promote best practices and workflows throughout the machine learning life cycle

Benefits

$160,000 - $220,000 base salary range (dependent on experience level and interview performance) + equity (incentive stock options, vested over 4 years) + benefits