R&D Intern - Fairness in Deep Learning

Conducts research on post-training processes for AI models, focusing on performance evaluation and optimization to enhance model safety and reliability.

This role conducts research on safety post-training methods for OpenAI's models, including RLHF, RLAIF, DPO, and PPO. The researcher will develop training interventions, build safety evaluations, and analyze model failures like hallucination and jailbreak robustness. This is a cross-functional role partnering with policy, legal, and trust & safety teams to launch safety improvements in production models.

ABOUT THE TEAM OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. ABOUT THE ROLE As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. IN THIS ROLE, YOU WILL: - Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. - Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. - Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions of fairness, and clearly document the assumptions, limitations, and residual risks of each approach. - Evaluate end-to-end human-AI decision systems, including model outputs, user behavior, human overrides, escalation pathways, and whether AI assistance changes the quality, consistency, or equity of decisions. - Develop evaluation approaches for generative and agentic AI, including test-set design, counterfactual testing, behavioral evaluation, human-rating studies, robustness testing, and analysis of disparate performance across populations and contexts. - Investigate the sources of observed disparities, including data representation, label and measurement bias, proxy variables, model design, decision thresholds, workflow design, and differential adoption or usage. - Partner with engineering, People Operations, Legal, Privacy, Security, and People Systems teams to recommend and evaluate mitigations such as data improvements, model changes, threshold ad

This role focuses on researching post-training methodologies to improve model quality and alignment. The researcher will analyze performance metrics and data to optimize model behavior, likely involving techniques like fine-tuning, RLHF, or evaluation frameworks.

This Partner role is responsible for growing Artefact's commercial business in Romandie, taking over an existing client portfolio and expanding it. The role involves opening new accounts, leading client executives through data and AI transformation, and growing the Lausanne team. Requires 12-15 years of consulting experience with a proven sales track record.

This role takes senior ownership of paid social activity across two key client accounts, combining account strategy, technical auditing, campaign management, and senior client management. The Senior Paid Social Account Manager must independently assess and optimize Meta environments, including Business Manager setup, campaign architecture, Pixel and Conversions API configuration, and audience strategy. Strong hands-on Meta expertise is essential, with TikTok Ads experience highly desirable, and the role includes developing testing roadmaps and coordinating with other media teams.

This internship involves supporting strategic planners in creative agency tasks, including client accompaniment, creative briefs, target analysis, and trend benchmarking. The intern will learn the planning craft by working alongside experienced planners and contributing to pitches and innovation projects. Requires a passion for advertising, strong curiosity, and ideally a previous planning internship.