5-7 years of experience in machine learning or AI research
Strong programming skills in research-focused environments
Experience in building benchmarks, datasets, and evaluation frameworks
Familiarity with modern ML systems, LLMs, and agent systems
Capable of designing experiments from ambiguous research problems
Ability to analyze and draw conclusions from complex, noisy data
Interest in compliance, trust, and safety within regulated domains
Responsibilities
Build proprietary benchmarks to evaluate fraud and risk models
Design offline and online evaluations for real customer tasks
Define and implement quality metrics for judgment systems
Investigate model failures and improve evaluation frameworks accordingly
Create reusable tools for quality assessment across product domains
Collaborate with cross-functional teams to enhance system quality
Foster a culture of scientific experimentation and iteration in evaluation processes
Benefits
Platinum-level medical, dental, and vision insurance
Unlimited PTO, sick leave, and parental leave
Up to $100 monthly for health and wellness reimbursements
401(k) retirement plan
Full Job Description
Role
We\'re looking for a Research Engineer to help define how we measure and improve model quality. You\'ll build the benchmarks, datasets, tooling, and evaluation loops that tell us whether our systems are actually getting better on the tasks that matter. This role sits at the center of research, product, and engineering. It is about creating rigorous, domain-specific evaluations that reflect real customer workflows, expose meaningful failure modes, and drive the next generation of model and agent improvements.
You\'re a fit if you:
Care deeply about craftsmanship and have strong opinions about model quality, measurement, and experimental rigor
Want to work on core model and agent behavior, not just surface-level product metrics
Are excited by the challenge of defining what \"good\" looks like in messy, high-stakes environments
Have strong engineering fundamentals and like building robust systems around ambiguous research problems
Thrive in environments where success criteria are initially underspecified and need to be sharpened through work
Are willing to do the work in the trenches: reviewing outputs, grading edge cases, curating datasets, and refining tasks until the evaluation actually measures what matters
Care deeply about building systems that protect people from fraud, scams, and abuse
What you\'ll do
Build proprietary benchmarks and datasets to evaluate models and model systems on fraud, identity, and risk workflows
Design and run offline and online evals that measure model performance on real customer tasks, not just abstract benchmarks
Define quality metrics for judgment systems, including precision, calibration, consistency, abstention, and failure handling
Study where models and agents break, and turn those failures into better evals, better datasets, and better training loops
Build reusable evaluation tools and quality building blocks that can be used across different product surfaces and workflows
Partner closely with research, engineering, product, and design to improve system quality through rigorous experimentation
Help create a strong culture of scientific experimentation, clear measurement, and continuous iteration
Push the boundary of how AI systems are evaluated in regulated, adversarial, and high-consequence environments
What success looks like
We have a clear, trusted view of how our systems perform across the workflows that matter most
Our evals predict real-world quality better than generic benchmarks
We identify meaningful failure modes earlier and improve system behavior faster
We develop differentiated datasets, benchmarks, and quality loops that compound over time
Research and engineering teams use your work to make better decisions about what to train, ship, and improve next
Variance becomes known for rigorous, domain-specific evaluation of judgment systems
Preferred background
Experience training, evaluating, or improving modern ML systems
Strong programming skills and comfort working in research-heavy codebases
Experience building benchmarks, datasets, evaluation pipelines, or quality systems
Familiarity with LLMs, agent systems, retrieval, post-training, or adjacent areas
Ability to design clean experiments and draw reliable conclusions from noisy results
Strong engineering judgment and a bias toward building
Interest in fraud, risk, trust and safety, compliance, or other regulated and adversarial domains
Our culture
We believe in ownership, urgency, and craft. We enjoy spirited debate, wild ideas, and building things we\'re proud of. We\'re fully in-person in San Francisco. What we offer
Competitive salary and meaningful equity
Platinum-level medical, dental, and vision insurance
Unlimited PTO, sick leave, and parental leave
Up to $100 per month in reimbursement for personal health and wellness expenses
