About Rapidata
Rapidata provides an API to humans that is revolutionizing the data generation and annotation industry. We deliver highly scalable, extremely fast human feedback that fuels the AI systems of the future, powering RLHF and DPO training data collection at internet speed for frontier AI labs. Our network reaches over 20 million active annotators across 192 countries, distributing micro-tasks ("Rapids") and returning verified labels in near real-time.
The Role
We're looking for a Reward Model Research Engineer to design, train, and productionize the reward models work alongside Rapidata's real-time human feedback for high-quality training signal for RLHF and DPO. This is a research-to-production role: you'll work on the modeling problems that determine whether noisy, large-scale human preference data becomes a reliable reward signal, including process reward models (PRMs) that score multi-step reasoning.
You'll be closing the loop between our human-in-the-loop data collection infrastructure and the reward models that consume it and accompany it, tackling generalization across generator models, robustness to annotator noise, and inference-time guidance techniques, then validating your models in a live system operating at global scale.
What You'll Do
Design, train, and evaluate reward models, including process reward models (PRMs), from large-scale human preference data
Build and improve inference-time guidance methods to make reward-guided generation more robust
Develop training setups that improve generalization across diverse generator/policy models
Translate reward modeling research into production-ready pipelines that plug directly into Rapidata's RLHF/DPO data flywheel
Collaborate with the data platform team to design data collection and active learning strategies that reduce reward model training bottlenecks
Rigorously benchmark reward models for accuracy, robustness, and reliability before deployment
Communicate findings clearly to both technical and non-technical stakeholders, including partner AI labs
What We're Looking For
Hands-on research experience building reward models for LLMs or diffusion models, ideally through an MSc/PhD thesis or equivalent applied research
Solid understanding of reinforcement learning fundamentals and RLHF/DPO training pipelines
Practical experience with inference-time guidance techniques and evaluating multi-step reasoning
Strong Python and deep learning framework skills (PyTorch)
Experience turning research prototypes into validated, production-ready systems
Solid statistical and mathematical foundation
Excellent English communication skills, both oral and written
Nice-to-Have
Experience with agentic system safety, guardrails, or LLM-based tool-calling agents
Publications or open-source contributions in reward modeling, reasoning, or reinforcement learning
Experience with hierarchical or model-based RL
Based in or willing to relocate to Zürich
What We Offer
Competitive salary and equity in a startup with strong growth, IP, and backing from top-tier VCs
Opportunity to join a fast-growing startup early, with an outsized opportunity to shape where the company goes
Opportunities for personal and professional growth as our team expands
Fun and open (startup) culture
Spacious mountain-view office near Sihlcity, Zürich, with terrace, table tennis, pizza oven, hammock, and BBQ
Hardware budget tailored to your preferences
Unlimited snacks and drinks of your choice