Machine Learning Engineer (Recommender Systems)
On-site · Washington, District of Columbia, United States
Job Summary
Design large-scale ML 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.
Required Qualifications
- 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
Apply with one swipe on Sorce. We auto-fill applications and apply on your behalf — no cover letters, no 40-minute forms.
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.