Job Description

This Turn2 client is a fast-growing consumer tech company that is hiring a Machine Learning Engineer to build real-time recommendation and ranking systems for a widely used AI-driven shopping assistant. This is a high-impact, high-ownership role ideal for someone who thrives in fast-paced environments, ships quickly, and wants to shape how users experience search, personalization, and pricing across millions of products.

Why This Role Stands Out:

• Immediate user impact: Your models power a real-world product used daily by a rapidly growing customer base.
• Full ownership: Architect, build, and ship systems from scratch in a fast-moving, product-centric culture.
• Startup velocity: Join a team of high-agency builders working to redefine how people shop.

What You’ll Do:

• Design large-scale systems to ingest and normalize data from 50+ external platforms, processing hundreds of millions of product listings.
• Build and deploy end-to-end ML pipelines for ranking, recommendation, and personalization.
• Collaborate with frontend and backend engineers to tightly integrate models into both web and app experiences.
• Prototype backend services that support rapid experimentation and user-facing iteration.
• Continuously optimize inference pipelines for latency, performance, and relevance.

What You Bring:

• 2+ years of hands-on experience building and deploying machine learning models in production.
• Proven ability to ship features in fast-moving, consumer-facing environments.
• Expertise in personalization, ranking models, embeddings, and real-time inference (PyTorch preferred).
• Experience building data pipelines for large-scale training and predictions.
• Proficient in Python and familiar with backend tech such as GraphQL, Node.js, gRPC, or Prisma.
• Solid understanding of cloud platforms (AWS, GCP, or Azure) and deployment best practices.
• A tinkering mindset—someone who builds side projects and thrives in early-stage product environments.

Bonus Points For:

• Experience with real-time recommendation or search ranking systems at scale.
• Exposure to fullstack development or a willingness to contribute across the stack.
• Familiarity with applied AI in consumer tech or e-commerce settings.