Machine Learning Engineer

Handshake is hiring a Machine Learning Engineer to support its Growth Relevance team in San Francisco, CA. In this full-time onsite role, you will build and improve machine learning systems that help optimize the user lifecycle, power personalized notifications, and support monetization strategies.

What you’ll do

• Develop and iterate on machine learning models and features that impact user experience across lifecycle, notifications, and monetization, with guidance from senior engineers.
• Collaborate with senior engineers, data scientists, and product managers to refine machine learning models that improve product features and overall user experience.
• Deepen technical expertise by working closely with experienced ML practitioners across model development, experimentation, and production deployment.

What you bring

• 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.
• Proficiency in Python, including hands-on experience with scikit-learn, PyTorch, or TensorFlow.
• Experience deploying and evaluating agentic workflows.
• A strong foundation in core ML concepts such as classification, regression, ranking, and model evaluation.

Tools and technologies

You’ll work with Python and frameworks including scikit-learn, PyTorch, and TensorFlow, along with cloud platforms such as GCP, AWS, and Azure.

Compensation and benefits

Salary: USD 150,000 - 189,000 per year. The role also includes offers equity.

• 401(k) match
• Financial coaching
• Paid parental leave, parental coaching
• Fertility benefits
• Medical, dental, and vision
• Mental health support
• $500 wellness stipend
• $2,000 learning stipend
• Ongoing development
• Internet and commuting support
• Free lunch/gym in the SF office
• Competitive compensation
• Equity

Extra credit

• Master’s degree or currently pursuing an advanced degree in a relevant field.
• Exposure to recommendations, personalization, NLP, deep learning, LLMs, or explainable AI.
• Familiarity with the ML lifecycle, such as experiment tracking, model monitoring, and feature pipelines.
• Experience with GCP, AWS, or Azure.
• Ability to clearly communicate technical work to diverse audiences.
• Collaborative experience working cross-functionally with product, analytics, and engineering teams.

Location: San Francisco, CA (onsite). Department: Engineering. Employment type: Full time.