Fast Facts

Join Handshake AI as a Senior AI Research Engineer to design and scale innovative systems for LLM post-training and evaluation, collaborating with top AI research labs and shaping the future of AI through data.

Responsibilities: Architect and optimize large-scale systems for post-training and evaluation, develop benchmarks, enforce data integrity methodologies, mentor junior engineers, and influence research engineering strategy.

Skills: Advanced proficiency in Python, experience in applied ML and large-scale distributed systems, strong expertise with PyTorch, and ability to manage technical projects end-to-end.

Qualifications: 5+ years in applied ML or related fields, experience with data pipelines and evaluation systems, and strong communication skills; extra credit for mentoring experience or open-source contributions.

Location: San Francisco, California, United States

Compensation: $260000 - $330000 / Annually

About Handshake AI

Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired.

Handshake AI is a human data labeling business that leverages the scale of the largest early career network.We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale.

This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.

Now’s a great time to join Handshake. Here’s why:

• Leading the AI Career Revolution: Be part of the team redefining work in the AI economy for millions worldwide.
• Proven Market Demand: Deep employer partnerships across Fortune 500s and the world’s leading AI research labs.
• World-Class Team: Leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, just to name a few.
• Capitalized & Scaling: $3.5B valuation from top investors including Kleiner Perkins, True Ventures, Notable Capital, and more.

About the Role

As a Senior Research Engineer, you’ll play a leading role in designing and scaling the infrastructure, systems, and frameworks that power the next generation of LLM post-training and evaluation. You’ll work closely with research scientists to define methodologies, push the boundaries of data quality and benchmark design, and mentor other engineers to raise the technical bar across the team. This role is ideal for someone who thrives at the intersection of research and large-scale engineering, with the ability to translate complex research insights into robust, production-grade systems. You will:

• Architect, implement, and optimize large-scale post-training systems and data processing pipelines, ensuring reliability, scalability, and performance.
• Lead the development of next-generation LLM benchmarks and evaluation frameworks, defining standards for measuring advanced reasoning, alignment, and knowledge capabilities.
• Design and enforce rigorous methodologies for verifying data integrity and quality across highly specialized datasets.
• Drive software/hardware performance optimization to accelerate experimentation and deployment (e.g., memory usage, training throughput, distributed systems).
• Partner with cross-disciplinary teams—including research scientists, domain experts, and product engineers—to validate and productionize model improvements.
• Mentor junior engineers and shape technical best practices for the post-training and evaluation engineering pod.
• Influence long-term research engineering strategy by identifying opportunities to systematize evaluation and data quality at scale.

Desired Capabilities

• Advanced proficiency in Python with a track record of building clean, maintainable, and performant codebases.
• 5+ years of experience in applied ML, large-scale distributed systems, or post-training infrastructure (RLHF, DPO, constitutional AI, etc.).
• Strong expertise with PyTorch and modern ML training frameworks; familiarity with distributed training and inference optimization.
• Proven experience designing and operating data pipelines, benchmark frameworks, or large-scale evaluation systems.
• Ability to drive technical projects end-to-end: from architecture design to implementation, scaling, and monitoring.
• Clear and confident communication skills; ability to collaborate across research and engineering disciplines and influence technical direction.

Extra Credit

• Experience leading small teams or mentoring engineers.
• Track record of open-source contributions in ML infrastructure or evaluation frameworks.
• Publications or public talks in applied ML, evaluation, or systems research.
• Passion for building responsible AI systems and considering the societal/ethical implications of model evaluation.

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: Stipends for home office setup, internet, commuting, and free lunch/gym in our SF office

🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days, winter #ShakeBreak where our whole office closes for a week!

🤝 Connection: Team outings & referral bonuses

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