ML Engineer I - Growth & Personalization at Scale (Remote)

Handshake; San Francisco (CA); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Equity

401(k) match

Parental leave

Mental health support

Learning stipend

SF office

Job summary

Handshake is hiring a Machine Learning Engineer I for the Growth Relevance team. You will contribute to AI-powered solutions guiding students toward meaningful career opportunities, deploying and improving systems built on embeddings, Graph Neural Networks, and rankers.

You will work with ML practitioners and product teams to advance lifecycle optimization, personalized notifications, and monetization strategies while upholding responsible AI practices and explainability.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, or a related field
• 1-3 years of experience in machine learning, data science, or a related area
• Proficient in Python, with hands-on experience in frameworks such as scikit-learn, PyTorch, or TensorFlow
• Experience with deploying and evaluating agentic workflows
• Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation

Responsibilities

• Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization — with guidance from senior engineers
• Collaborate with senior engineers, data scientists, and product managers to improve product features and user experience
• Learn and grow by working with experienced ML practitioners, adopting best practices in model development, experimentation, and production deployment

Skills

Machine learning

Python

Collaboration

Learning mindset

Model evaluation

Education

Bachelor’s degree in Computer Science or Data Science

Tools

scikit-learn

PyTorch

TensorFlow

Job description

Handshake is hiring a Machine Learning Engineer I for the Growth Relevance team. You will contribute to AI-powered solutions guiding students toward meaningful career opportunities, deploying and improving systems built on embeddings, Graph Neural Networks, and rankers.

You will work with ML practitioners and product teams to advance lifecycle optimization, personalized notifications, and monetization strategies while upholding responsible AI practices and explainability.