Handshake - Machine Learning Engineer I
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Requirements
• MS or PhD degree in a relevant field. • Experience in applied ML in domains such as recommendations, personalization, search, NLP, or graph-based learning. • Familiarity with generative recommendation approaches — including semantic item tokenization (RQ-VAE, residual quantization), unified retrieval-ranking architectures, or sequential recommendation models — even if through research or coursework rather than production. • Exposure to preference-aligned training objectives (RLHF, DPO, reward modeling) and interest in applying them to multi-objective recommendation settings. • Hands-on experience with Graph Neural Networks or graph-based representation learning for user or item modeling. • Familiarity with dense retrieval, two-tower architectures, or embedding-based candidate generation at scale. • Experience with ML lifecycle management including experiment tracking, feature engineering pipelines, and production monitoring. • Experience with cloud infrastructure such as GCP, AWS, or Azure in the context of ML workflows. • Publications or contributions at venues such as SIGIR, KDD, WSDM, RecSys, NeurIPS, or ICML — particularly in retrieval, ranking, or generative modeling. • Strong communication skills with the ability to present technical work clearly to both technical and non-technical audiences.
Responsibilities
• Owner: Take end-to-end ownership of ML models and features — from problem framing and experimentation through deployment and production monitoring — with growing autonomy over time. • Owner: • Innovator: Develop and iterate on machine learning models that improve core relevance and network-driven signals, including graph-based and embedding-based approaches. • Innovator: • Collaborator: Partner closely with senior engineers, data scientists, and product managers to design experiments, interpret results, and translate findings into product impact. • Collaborator: • Desired Capabilities • Bachelor's degree in Computer Science, Data Science, or a related technical field. • 1–3 years of industry or research experience in machine learning or a related area. • Proficiency in Python and hands-on experience with ML frameworks such as PyTorch or TensorFlow. • Solid understanding of core ML concepts: ranking, classification, regression, model evaluation, and validation. • Familiarity with software engineering best practices including version control, testing, and code reviews. • Experience with SQL and data analysis techniques.
Benefits
• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel • Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions • Build a massive, fast-growing business with billions in revenue • Handshake is hiring a Machine Learning Engineer I for the Network & Core Relevance team. The recommender systems playbook that dominated the last decade is being rewritten, and we're hiring the engineers who will lead that rewrite. • We're rebuilding our core discovery engine around generative recommendation architectures: unified retrieval and ranking under shared transformer backbones, semantic item tokenization, graph-aware representation learning, and preference-aligned training objectives. This is the most significant architectural shift in recommender systems in a generation, and it's happening in production. • In this role, you'll take end-to-end ownership of ML models and features that determine how students and employers find each other. You'll work on hard problems — behavioral signal sparsity in a search domain, cold-start at institutional scale, multi-objective optimization across a three-sided marketplace — and you'll be expected to take big swings on them. • 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 • Ownership: • 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching • Financial Wellness • 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching • Family Support: • 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend • Wellbeing: • 📚 Growth: $2,000 learning stipend, ongoing development • Growth: • 💻 Remote & Office: Internet, commuting, and free lunch/gym in our SF office • Remote & Office: • 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days • Time Off: • 🤝 Connection: Team outings & referral bonuses • Connection: • Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.
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