Staff AI Research Scientist - Data Quality, Handshake AI

Sorry, this job was removed at 02:10 a.m. (CST) on Thursday, Jan 29, 2026
2 Locations
In-Office or Remote
Edtech • Enterprise Web • HR Tech • Software
Handshake is the number one site for college students to find a job.
The Role
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

As a Staff Research Scientist, you will play a pivotal role in shaping the future of large language model (LLM) alignment by leading research and development at the intersection of data quality and post-training techniques such as RLHF, preference optimization, and reward modeling.

You will operate at the forefront of model alignment, with a focus on ensuring the integrity, reliability, and strategic use of supervision data that drives post-training performance. You’ll set research direction, influence cross-functional data standards, and lead the development of scalable systems that diagnose and improve the data foundations of frontier AI.

You will:

  • Lead high-impact research on data quality frameworks for post-training LLMs — including techniques for preference consistency, label reliability, annotator calibration, and dataset auditing.

  • Design and implement systems for identifying noisy, low-value, or adversarial data points in human feedback and synthetic comparison datasets.

  • Drive strategy for aligning data collection, curation, and filtering with post-training objectives such as helpfulness, harmlessness, and faithfulness.

  • Collaborate cross-functionally with engineers, alignment researchers, and product leaders to translate research into production-ready pipelines for RLHF and DPO.

  • Mentor and influence junior researchers and engineers working on data-centric evaluation, reward modeling, and benchmark creation.

  • Author foundational tools and metrics that connect supervision data characteristics to downstream LLM behavior and evaluation performance.

  • Publish and present research that advances the field of data quality in LLM post-training, contributing to academic and industry best practices.

Desired Capabilities
  • PhD or equivalent experience in machine learning, NLP, or data-centric AI, with a track record of leadership in LLM post-training or data quality research.

  • 5 years of academic or industry experience post-doc

  • Deep expertise in RLHF, preference data pipelines, reward modeling, or evaluation systems.

  • Demonstrated experience designing and scaling data quality infrastructure — from labeling frameworks and validation metrics to automated filtering and dataset optimization.

  • Strong engineering proficiency in Python, PyTorch, and ecosystem tools for large-scale training and evaluation.

  • A proven ability to define, lead, and execute complex research initiatives with clear business and technical impact.

  • Strong communication and collaboration skills, with experience driving strategy across research, engineering, and product teams.

Extra Credit
  • Experience with data valuation (e.g. influence functions, Shapley values), active learning, or human-in-the-loop systems.

  • Contributions to open-source tools for dataset analysis, benchmarking, or reward model training.

  • Familiarity with evaluation challenges such as annotation disagreement, subjective labeling, or multilingual feedback alignment.

  • Interest in the long-term implications of data quality for AI safety, governance, and deployment ethics.

We Offer

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.

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The Company
HQ: San Francisco, CA
700 Employees
Year Founded: 2014

What We Do

Handshake is the #1 place to launch a career with no connections, experience, or luck required. The platform connects up-and-coming talent with 650,000+ employers - from Fortune 500 companies like Google, Nike, and Target to thousands of public school districts, healthcare systems, and nonprofits. Earlier this year, we announced our $200M Series F funding round. This Series F fundraise and new valuation of $3.5B will fuel Handshake’s next phase of growth and propel our mission to help more people start, restart, and jumpstart their careers.

Why Work With Us

How someone builds their career is foundational. We believe in working with the higher education community to help students build meaningful careers. We are at the nexus of universities, students and employers— and we’re able to connect the very best pieces of each side. We’re proud to create a community where students can be more successful.

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