Senior Machine Learning Engineer, AI Insights at Affinity

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

09 Feb, 26

Salary

210000.0

Posted On

11 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Software Engineering, Recommendation Systems, Information Retrieval, Python, ML Frameworks, Collaborative Filtering, Feature Engineering, Model Selection, Deployment, Model Evaluation, Data Engineering, Observability Tools, Text-to-SQL, CI/CD, Data Curation

Industry

Software Development

Description

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 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. What you'll enjoy at Affinity: We live our values: As owners, we take pride in everything we do. We embrace a growth mindset, engage in respectful candor, act as playmakers, and "taste the soup" by diving deep into experiences to create the best outcomes for our colleagues and clients. Health Benefits: We cover your medical, dental, and vision insurance premiums with comprehensive PPO, HDHP and HMO options (in CA), and offer flexible personal & sick days to support your well-being. Retirement Planning: We offer a 401(k) plan to help you plan for your future. Learning & Development: We provide an annual education budget and a comprehensive L&D program. Wellness Support: We reimburse monthly for things like home internet, meals, and wellness memberships/equipment to support your overall health and happiness. Team Connection: Virtual team-building activities and socials to keep our team connected, because building strong relationships is key to success. Please note that the role compensation details below reflect the base salary only and do not include any equity or benefits. This represents the salary range that Affinity believes, in good faith, at the time of this posting, that it will pay for the posted job. A reasonable estimate of the current range is $106,200 to $210,000 USD. Within the range, individual pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. About Affinity With more than 3,000 customers worldwide and backed by some of Silicon Valley's best firms, Affinity has raised $120M to empower dealmakers to find, manage, and close more deals. How? Our Relationship Intelligence platform uses the wealth of data exhaust from trillions of interactions between Investment Bankers, Venture Capitalists, Consultants, and other strategic dealmakers to deliver automated relationship insights that drive over 450,000 deals every month. We are are proud to have received Inc. and Fortune Best Workplaces awards as well as to be Great Places to Work certified for the last 5 years running. Join us on our mission to make it possible for anyone to cultivate and fully harness their network to succeed. We use E-Verify Our company uses E-Verify to confirm the employment eligibility of all newly hired employees. To learn more about E-Verify, including your rights and responsibilities, please visit www.dhs.gov/E-Verify.

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Responsibilities

Own the full ML lifecycle from ideation to production, including feature engineering and model evaluation. Collaborate with cross-functional teams to build robust systems that deliver actionable insights to customers.