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. Partner with business leaders and leverage cutting-edge machine learning and engineering resources to transform user experience. You will collaborate with engineers, data scientists, and product managers to improve algorithms, design and maintain scalable machine learning systems, and develop algorithms using state-of-the-art techniques. Improve core machine learning infrastructure and apply software engineering best practices. Define and promote best practices throughout the machine learning life cycle. This role offers a competitive salary, equity, and benefits.

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