Senior ML Engineer - Ranking & Recommendations (Remote)

Affinity.co; San Francisco (CA); Hybrid; USD 160,000 - 235,000; Full time

Benefits offered by this job

Health benefits

401(k) program

Learning & Development budget

Wellness & remote support

Job summary

Affinity is seeking a Senior Machine Learning Engineer to join the AI Platform team in San Francisco, New York, or US Remote. You will design and deploy ML-powered private capital relationship insights and recommendations, collaborating with data engineers, software engineers, and product managers to scale the platform.

You will own end-to-end ML pipelines, from feature engineering to model deployment and observability, focusing on information retrieval and ranking systems for large-scale

Qualifications

• 5+ years of software engineering and/or ML experience in production.
• Hands-on experience building ranking or recommendation systems at scale.
• Strong understanding of ML techniques including clustering and decision trees.
• Experience serving ML models for streaming and batch inference at scale.

Responsibilities

• Own the full ML lifecycle from ideation to production, including feature engineering, deployment, and observability.
• Translate business needs into robust ML system designs and requirements.
• Build and scale ranking and recommendation infrastructure, starting with off-the-shelf models and evolving to custom solutions.
• Tackle information extraction, storage, and retrieval challenges across structured and unstructured data.
• Collaborate with product, infra, data engineering, and software teams to deploy high-scale ML systems.

Skills

5+ years experience

Ranking systems

Python

ML in production

Tools

PyTorch

Scikit-learn

Vector/Graph DBs

Job description

Affinity is seeking a Senior Machine Learning Engineer to join the AI Platform team in San Francisco, New York, or US Remote. You will design and deploy ML-powered private capital relationship insights and recommendations, collaborating with data engineers, software engineers, and product managers to scale the platform.

You will own end-to-end ML pipelines, from feature engineering to model deployment and observability, focusing on information retrieval and ranking systems for large-scale