Machine Learning Engineer I

Handshake

🏠 Remote
📍San Francisco, CA
Posted May 19, 2026

Job Overview

Position

Machine Learning Engineer I

Company

Handshake

Location

San Francisco, CA

Work Type

Remote

Job ID

li-4398057615

Job Description

About Handshake
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.

Why Join Handshake Now

  • Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel

  • Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions

  • Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders

  • Build a massive, fast-growing business with billions in revenue

About Handshake AI
Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

The ML Opportunity At Handshake
Machine Learning is at the heart of Handshake's mission to democratize access to opportunity. Every student deserves to be seen, not just by any employer, but by the
*right*
one. Machine learning is how we personalize discovery, match intent with opportunity, and drive better outcomes at scale.

What Makes This Role Unique

  • End-to-end ownership: You'll work on the entire ML lifecycle, from data to deployment, not just one part

  • Product impact: Your ML systems directly impact millions of users and drive critical business metrics

  • Cutting-edge infrastructure: Work with embedding-based retrieval, Graph Neural Networks, and multi-stage rankers

  • Responsible AI: Contribute to explainability, fairness, and quality practices

  • Scale: Billions of data points powering our ML systems

  • World-class team: Leadership from Scale AI, OpenAI, xAI, Notion, Coinbase, and Palantir

What You'll Do

  • 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.

Desired Capabilities

  • 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

Perks
Handshake delivers benefits that help you feel supported—and thrive at work and in life.

*The below benefits are for full-time US employees.*
🎯
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

Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.

Compensation Range: $151K - $189K

Interview Prep

AI-powered insights to help you prepare

Key Skills

Required:
Preferred:

Practice Questions

💡Technical Questions (3)
  • 1.Handshake uses multi-stage rankers and embedding-based retrieval to match students with the right employers. Can you walk me through how you would design a two-stage recommendation system for this use case?
  • 2.Since Handshake powers billions of data points and emphasizes responsible AI, how do you evaluate a classification model beyond standard accuracy, especially when dealing with potential class imbalance in job matching?
  • 3.Given Handshake AI's work with frontier labs and LLMs, how would you approach incorporating LLM-generated features or embeddings into an existing personalization model?
🎯Behavioral Questions (3)
  • 1.This role requires end-to-end ownership from data to deployment. Tell me about a time you took a machine learning model from the experimentation phase all the way to production.
  • 2.As a Learner, you'll be working alongside experienced ML practitioners from top-tier companies. Can you share an example of a time you had to quickly get up to speed on a new ML concept or technology?
  • 3.You'll be collaborating cross-functionally with product managers, data scientists, and engineers. Tell me about a time you had to translate complex technical ML work to a non-technical stakeholder.
🧩Situational Questions (2)
  • 1.You deploy a new ranking model for job recommendations, and the next day, product managers report that user engagement has dropped significantly. How do you approach diagnosing and resolving this?
  • 2.A product manager wants to add a new feature to improve notification relevance, but the senior engineer you are partnering with thinks the current infrastructure can't handle the real-time feature pipeline. How do you navigate this?

Resume Keywords

Make sure these keywords appear on your resume

Machine Learning LifecycleRecommendation SystemsEmbedding-based RetrievalGraph Neural NetworksPyTorchModel EvaluationResponsible AICross-functional CollaborationPersonalizationProduction DeploymentExperiment TrackingRanking Models

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