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Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.
In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.
Machine Learning Engineer I
Handshake
On-site
San Francisco, CA, United States
Full-time
$151,000 -
$189,000
About Handshake
About the Role
We're looking for a Machine Learning Engineer to help us connect millions of job seekers with the opportunities that are right for them. Machine learning is how we personalize discovery, match intent with opportunity, and drive better outcomes at scale — and you'll be hands-on building the systems that make that happen.
In this role, you'll work across the entire ML lifecycle, from data to deployment, developing and iterating on models that directly shape the user experience across lifecycle, notifications, and monetization. You'll work with modern infrastructure like embedding-based retrieval, Graph Neural Networks, and multi-stage rankers, applied to billions of data points. Alongside that, you'll help advance our responsible AI practices around explainability, fairness, and quality.
In this role, you'll work across the entire ML lifecycle, from data to deployment, developing and iterating on models that directly shape the user experience across lifecycle, notifications, and monetization. You'll work with modern infrastructure like embedding-based retrieval, Graph Neural Networks, and multi-stage rankers, applied to billions of data points. Alongside that, you'll help advance our responsible AI practices around explainability, fairness, and quality.
Qualifications
Bachelor’s or advanced degree in Computer Science, Data Science, or a related field.
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.
Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation.
Extra Credit
Exposure to areas such as recommendations, personalization, NLP, deep learning, LLMs, or explainable AI.
Familiarity with the ML lifecycle (e.g., experiment tracking, model monitoring, feature pipelines).
Experience with cloud platforms (GCP, AWS, or Azure).
Clear communicator, able to translate technical work for diverse audiences.
Collaborative mindset with experience working cross-functionally with product, analytics, and engineering teams.
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.
Strong foundation in core ML concepts, including classification, regression, ranking, and model evaluation.
Extra Credit
Exposure to areas such as recommendations, personalization, NLP, deep learning, LLMs, or explainable AI.
Familiarity with the ML lifecycle (e.g., experiment tracking, model monitoring, feature pipelines).
Experience with cloud platforms (GCP, AWS, or Azure).
Clear communicator, able to translate technical work for diverse audiences.
Collaborative mindset with experience working cross-functionally with product, analytics, and engineering teams.
Responsibilities
Innovator: Develop and iterate on machine learning models and features that directly influence user experience across lifecycle, notifications, and monetization — with guidance from senior engineers.
Collaborator: Partner with senior engineers, data scientists, and product managers to develop and iterate on machine learning models that improve product features and user experience.
Learner: Grow your technical depth by working alongside experienced ML practitioners, picking up best practices in model development, experimentation, and production deployment.
Collaborator: Partner with senior engineers, data scientists, and product managers to develop and iterate on machine learning models that improve product features and user experience.
Learner: Grow your technical depth by working alongside experienced ML practitioners, picking up best practices in model development, experimentation, and production deployment.
Benefits
Ownership: Equity in a fast-growing company.
Financial Wellness: 401(k) match, competitive compensation, financial coaching.
Family Support: Paid parental leave, fertility benefits, parental coaching.
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend.
Growth: $2,000 learning stipend, ongoing development.
Remote & Office: Internet, commuting, and free lunch/gym in our SF office.
Time Off: Flexible PTO, 15 holidays + 2 flex days.
Connection: Team outings & referral bonuses.
Financial Wellness: 401(k) match, competitive compensation, financial coaching.
Family Support: Paid parental leave, fertility benefits, parental coaching.
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend.
Growth: $2,000 learning stipend, ongoing development.
Remote & Office: Internet, commuting, and free lunch/gym in our SF office.
Time Off: Flexible PTO, 15 holidays + 2 flex days.
Connection: Team outings & referral bonuses.
