Senior Machine Learning Engineer

About the role

Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. We offer our users insights and visibility into their team's network to nurture and tap into opportunities.

Responsibilities

• 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.

Qualifications

• 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.

Pay

$160,000 to $235,000 USD

Schedule

Hub-hybrid model: In-office 2–3 days per week, typically Tuesday through Thursday. Remote option available for those located in San Francisco or New York.