Slickdeals7h ago

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Sr. ML Infrastructure Engineer II, Personalization

United States·San Mateosenior

OtherMl Infrastructure Engineer

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Quick Summary

Key Responsibilities

This role spans the full ML lifecycle for recommendations — from candidate generation through ranking, serving, and online evaluation. Concretely: Modeling Design, train,

Technical Tools

OtherMl Infrastructure Engineer

About Slickdeals:

We believe shopping should feel like winning. That’s why 10 million people come to Slickdeals to swap tips, upvote the best finds, and share the thrill of a great deal. Together, our community has saved more than $10 billion over the past 26 years.

We’re profitable, passionate, and in the middle of an exciting evolution—transforming from the internet’s most trusted deal forum into the go-to daily shopping destination. If you thrive in a fast-moving, creative environment where ideas turn into impact fast, you’ll fit right in.

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:

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@k, 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

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

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

~1 min read

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, CA

Hybrid 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

Requirements

~1 min read

Location & Eligibility

Where is the job

San Mateo, United States

On-site at the office

Who can apply

Listing Details

Posted: May 18, 2026
First seen: May 19, 2026
Last seen: May 19, 2026

Posting Health

Days active: 0
Repost count: 0
Trust Level: 60%
Scored at: May 19, 2026

Signal breakdown

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Apply for this position

Slickdeals

greenhouse

Slickdeals is a community-driven deal-sharing platform where millions of users discover, vet, and vote on the best online deals, having collectively saved over $10 billion since 1999.

Employees

125

Founded

1999

Domain

Jobs

External application · ~5 min on Slickdeals's site

Please let Slickdeals know you found this job on Jobera.