Senior Machine Learning Engineer, AI Insights

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 Insights team, which owns the 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, not research. You will play a key role in advancing our ML Engineering capabilities, particularly in recommendation systems and information retrieval.  

What you’ll be doing:

• 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 sophisticated recommendation and ranking systems: Design and implement ranking and recommendation systems using techniques such as learn-to-rank (LTR) and collaborative filtering.
• 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 teams (product management, infrastructure, data engineering, and software engineering) to build robust, high-scale systems that underlie all of our data processing and ML Operations.

Qualifications

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role, but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• 5+ years of experience in software engineering and/or Machine Learning experience in applying machine learning in production. 

Recommendation Systems & Information Retrieval:

• Hands-on experience developing recommendation and ranking systems at scale, using techniques such as:
• Learn-to-rank (LTR) algorithms, including RankNet, LambdaRank, or similar approaches
• Collaborative filtering and content-based filtering
• Reranking strategies and hybrid search implementations
• Information retrieval and relevance scoring
• Solid understanding of machine learning techniques, including clustering and decision forests.

ML Engineering:

• Experiences with serving ML models for streaming and batch inference at scale.
• Experience with vector databases (milvus, weaviate) or graph database (Neo4j)
• 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. 
• Develop AI applications powered by LLMs and agent-based systems
• Familiar with modern LLM development frameworks:
• Feature development: LangChain, LlamaIndex, or similar orchestration frameworks
• Evaluation & monitoring: LangSmith, Weights & Biases, TruLens, DeepEval, Azure AI, or equivalent tools
• Experience with text-to-SQL (text2sql) generation or similar natural language to structured query tasks
• Experience with packaging, CI/CD and pipeline automation. 

Tech stack: Our ML pipeline manages multiple Python services that support