About Handshake

Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.

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

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

• Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others

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

About the Role

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.

Your Role

• 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.

• Innovator: Develop and iterate on machine learning models that improve core relevance and network-driven signals, including graph-based and embedding-based approaches.

• Collaborator: Partner closely with senior engineers, data scientists, and product managers to design experiments, interpret results, and translate findings into product impact.

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.

Preferred Qualifications

• 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.