Senior Machine Learning Engineer, AI Platform

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.

Requirements

• Required:
• 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.
• 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.
• Tech stack : Our ML pipeline manages multiple Python services that support various AI features, including utilizing OCR to extract information from unstructured data, serving embedding models to vectorize chunks, and ranking a list of recommendations based on relevance and user preference.
• How we work:
• Our culture is a key part of how we operate, as well as our hiring process:
• We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver value to our customers.
• We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with.
• We make data-driven decisions and make the best decision for the moment based on the information available.
• If you'd want to learn more about our values click here .
• Work Location: San Francisco, New York, or US Remote (Affinity is registered to employ in certain U.S. states)

Benefits

Additional Information

Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into the opportunities in their team's network. This role is part of the AI Platform team, which owns the AI services that power Affinity's industry-leading relationship intelligence platform. We extract and retrieve information from billions of structured and unstructured data points to deliver actionable insights to customers. As a Senior Machine Learning Engineer , you will collaborate with data engineers, software engineers, and product managers to shape the future of private capital's leading CRM platform. You will design and build AI systems that efficiently uncover insights from compelling business interaction data - an exciting and unique opportunity within the industry. This is an applied machine learning position with a strong emphasis on engineering, rather than research. You will play a key role in advancing our ML Engineering capabilities, particularly in information retrieval and eventually recommendation systems.

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