The Purpose:The Personalization team owns the systems that decide what each Slickdeals user sees, from homepage and feed rankings to deal recommendations across the site and in lifecycle channels. Personalization is one of our highest-leverage investments: it directly drives engagement, retention, and revenue across tens of millions of monthly users.
We're hiring a Sr. ML Engineer II who can operate end-to-end across the recommendation stack. This is a true hybrid role with roughly half modeling and half infrastructure. You will design and ship recommendation models (retrieval, ranking, and re-ranking) and build the production ML systems that train, serve, and evaluate them at scale. You'll work closely with data scientists, product engineers, and the Search & Discovery and Shopping Graph teams.
You will be building products using technologies such as AWS SageMaker, PyTorch, TensorFlow, vector databases, Elasticsearch, HBase, SQS/Kafka, REST web services, LLMs, and more.
What You'll Do:This role spans the full ML lifecycle for recommendations - from candidate generation through ranking, serving, and online evaluation. Concretely:
Modeling- Design, train, and ship recommendation models including two-tower / dual-encoder retrieval, neural ranking, and re-ranking models
- Build embedding pipelines for users, deals, merchants, and content; iterate on representation learning approaches
- Improve candidate generation strategies, including ANN-based retrieval over learned embeddings
- Define and run rigorous offline evaluation (recall[redacted], NDCG, MAP, calibration) and partner with data science to design online A/B tests
- Partner with product and data science on personalization surfaces - homepage, feeds, deal pages, search re-ranking, and lifecycle channels
Infrastructure- Build and own end-to-end ML pipelines for recommendations: data preparation, training, evaluation, deployment, and monitoring
- Design and operate low-latency model serving for high-QPS recommendation traffic
- Build feature pipelines and feature-store patterns that maintain online/offline parity
- Design, architect, and build reliability, observability, and utilization infrastructure for the recommendations stack
- Improve training cost, turnaround time, and reproducibility on the ML platform; collaborate with data scientists to unblock experimentation
Cross-cutting- Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence
- Collaborate with engineers within the team and across the company to solve complex data problems at scale
- Write high-quality, product-level code that is easy to maintain and test following standard methodologies
What We're Looking For:- 8+ years of relevant professional experience
- Demonstrated experience designing, training, and shipping recommendation systems in production - not just classifiers or general ML
- Hands-on experience with deep learning for recsys: two-tower / dual-encoder models, embedding-based retrieval, neural ranking, or similar
- Strong ML fundamentals: model evaluation methodology, A/B testing, debugging models at scale, handling data and label quality issues
- Proficiency with ML modeling frameworks (PyTorch and/or TensorFlow) (5+ yrs)
- Experience with model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton, or comparable custom serving infrastructure)
- Experience with vector retrieval / ANN at scale (e.g., FAISS, ScaNN, OpenSearch k-NN, Pinecone, Weaviate, or similar)
- Experience working with cloud data processing technologies such as Apache Spark, Elasticsearch, Presto, SQL (3+ yrs)
- Proficiency in at least two of: Linux, Ansible, Docker, Kubernetes (5+ yrs)
- Experience in distributed computing (7+ yrs)
- Experience working with AWS or similar cloud infrastructure (5+ yrs)
- Experience with hardware / resource management for ML training and/or deployment
- Knowledge of the open source landscape with judgment on when to choose open source versus build in-house
- Excellent analytical and problem-solving skills
- Comfort operating across both modeling and infrastructure - this is not a pure modeling or pure platform role
Nice to have:- Experience with feature stores (Feast, Tecton, or custom)
- Experience with real-time / streaming feature engineering
- Experience with LLM-augmented retrieval or hybrid retrieval architectures
- E-commerce, content, or marketplace recommendation domain experience
LOCATION: San Mateo, CAHybrid schedule visiting our San Mateo office three days a week (Tues-Thurs).
Slickdeals Compensation, Benefits, Perks:The expected base pay for this role is between $170,000 - $220,000. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Exact compensation will be discussed during the interview process and tailored to the candidate's qualifications.
- Competitive base salary, annual bonus, and equity package
- Competitive paid time off in addition to holiday time off
- A variety of healthcare insurance plans to give you the best care for your needs
- 401K matching above the industry standard
- Professional Development Reimbursement Program
Work AuthorizationCandidates must be eligible to work in the United States.
