Related skills
aws kubernetes tensorflow pytorch faiss📋 Description
- End-to-end ML lifecycle for recommendations (gen, ranking, serving, online evaluation)
- Design, train, and ship two-tower/dual-encoder models and embedding pipelines
- Build production ML pipelines for data prep, training, evaluation, deployment, and monitoring
- Low-latency model serving for high-QPS recommendation traffic
- Collaborate with data scientists and product engineers across teams
- San Mateo, CA location with a hybrid schedule in the office
🎯 Requirements
- 8+ years of relevant professional experience
- Designing, training, and shipping production recommender systems
- Deep learning for recsys: two-tower/dual-encoder models and embedding retrieval
- Proficiency with PyTorch and/or TensorFlow (5+ yrs)
- Experience with vector retrieval/ANN at scale (FAISS, ScaNN, OpenSearch k-NN, Pinecone, Weaviate)
- Experience with AWS or similar cloud infrastructure (5+ yrs)
🎁 Benefits
- Hybrid schedule: in-office three days a week (Tues-Thurs) in San Mateo
- Variety of healthcare insurance plans
- 401K matching above the industry standard
- Professional Development Reimbursement Program
Meet JobCopilot: Your Personal AI Job Hunter
Automatically Apply to Engineering Jobs. Just set your
preferences and Job Copilot will do the rest — finding, filtering, and applying while you focus on what matters.
Help us maintain the quality of jobs posted on Empllo!
Is this position not a remote job?
Let us know!