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About the Role
As a Senior Machine Learning Engineer on the AI Platform team, you will design and build AI systems that efficiently uncover insights from business interaction data. This role emphasizes applied machine learning and engineering, focusing on information retrieval and recommendation systems.
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
- 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.
Skills
- Python
- PyTorch
- Scikit-learn
- Information Retrieval
- Recommendation Systems
- Ranking Systems
- Clustering
- Decision Trees
- MLOps
- Vector Databases
- Graph Databases
- Observability Tools
- A/B Testing
- CI/CD
- Pipeline Automation
Location
- San Francisco
- New York
- US Remote
Work Type
- Hub-hybrid (San Francisco, New York)
- Remote (US)
Experience Level
- Senior
Salary/Compensations
- $160,000 to $235,000 USD
Benefits
- Medical, dental, and vision insurance premiums covered
- Flexible personal & sick days
- 401(k) plan
- Annual education budget
- Comprehensive L&D program
- Monthly reimbursement for home internet, meals, and wellness memberships/equipment
- Virtual team-building activities and socials
About the Company
- Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph.
- 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 have more than 3,000 customers worldwide and are backed by some of Silicon Valley's best firms.
- Affinity has raised $120M to empower dealmakers to find, manage, and close more deals.
- Proud recipients of Inc. and Fortune Best Workplaces awards.
- Great Places to Work certified for the last 5 years running.
Equal Opportunity
- 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.