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# Senior Machine Learning Engineer

- **Company:** Affinity, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $235,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Continuous Integration, Information Engineering, Computer Data Storage, Graph Database, Information Extraction, Information Retrieval, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Software Engineering, Data Streaming, Unstructured Data, Data Processing, Feature Engineering, Pytorch, Delivery Pipeline, Grafana, Model Validation, Scikit Learn, Machine Learning Operations
- **Published:** July 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=65340a773aecf851

## About the Role

* 5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production.
 * Hands-on experience developing ranking or recommendation systems from scratch, deployed at scale using techniques such as learn-to-rank, explainable recommendations
 * Strong understanding of machine learning techniques, including clustering and decision trees
 * Experience with serving ML models for streaming and batch inference at scale.
 * Experience with vector or graph databases.
 * Proficiency in Python and modern ML frameworks (PyTorch, Scikit-learn, or similar).
 * Track record of building maintainable, testable, and production-grade codebases.
 * Experience with observability tools for online and offline model evaluation, A/B testing, and tracing for AI applications.

Nice to Have:

 * Experience with dataset engineering, including data curation, augmentation, and synthesis, to assist ML model improvement.
 * Experience with graph-based recommendation systems, such as graph NN.
 * Experience with packaging, CI/CD and pipeline automation.

## Description

* Own the full ML lifecycle: Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation.
 * Translate business needs into ML solutions: Gather product requirements and translate them into robust ML system design requirements.
 * Build recommendation and ranking systems: Architect and launch ranking and recommendation infrastructure from scratch, initially via integrated off-the-shelf models, and evolving to targeted and customized solutions in the long term.
 * Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data.
 * Collaborate cross-functionally: Partner with cross-functional (product, infra, data engineering, and software engineering) teams to build robust, high-scale systems that underlie all of our data processing and ML Operations.


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