Resaro was founded on the belief that AI will change the world in ways we cannot even imagine, but every new technology needs safeguards to advance.
About the Role
We are looking for an AI Scientist with a deep foundation in Machine Learning and a passion for the
frontiers of Reinforcement Learning. You will be responsible for building a robust framework to stress-test and audit Reinforcement Learning systems, making sure they are fit for purpose and safe to be deployed. We value strong technical ability and real world experience and there will be room to solve challenging problems and adopt cutting edge technology into business applications.
YOU WILL
- Implement Reinforcement Learning Evaluation: Execute a dedicated work plan to build frameworks that evaluate the performance, safety, and alignment of RL agents.
- Build Uncertainty-Aware Tools: Use Bayesian ML models (GPs, BNNs) to create metrics for model confidence and risk.
- Develop Testing Infrastructure: Design and set up the debugging and automated testing frameworks required to evaluate non-deterministic systems.
- Execute Technical Evaluations: Perform "red-team" tests and benchmarks on models usingTrust Region methods (PPO) and RL from Human Feedback (RLHF).
- Master the RL Stack: Work across the entire stack, from environment interfacing to policy optimization, with the opportunity to grow into Multi-Agent RL (MARL) technologies.
YOU ARE ABLE TO
- Execute with Precision: Take high-level theoretical designs and turn them into clean, production-ready code.
- Think from First Principles: Use your technical background to solve challenging problems from the ground up.
- Collaborate & Learn: Work effectively under senior mentorship to adopt new research into business applications.
- Navigate Uncertainty: Thrive in a fast-paced environment where the mission of AI safety is paramount.
YOU HAVE
- Mastery of the ML Stack: Strong proficiency in Python, NumPy, and PyTorch.
- Theoretical Foundation: A background in ML theory, Mathematics, or Physics.
- Bayesian Knowledge: Experience with Bayesian ML models (e.g., Gaussian Processes, Bayesian Neural Networks).
- RL Specialization: Practical experience or familiarity with Trust Region methods (PPO) and RL from Human Feedback (RLHF).
- Engineering Rigor: Proven ability in debugging and setting up automated testing frameworks.
NICE TO HAVE
- Knowledge of Multi-Agent RL (MARL) technology.
- Interest in applied research on the safe and responsible use of AI.
ABOUT US
Resaro is a global AI Assurance company, pioneering the field of AI testing and evaluation. We are a team of AI experts, engineers, and data scientists. Our mission is to ensure an AI market worthy of trust.
Resaro is an Equal Opportunity Employer. We respect each individual and support the diverse cultures, perspectives, skills and experiences within our teams.
Skills Required
- Strong proficiency in Python, NumPy, and PyTorch.
- Background in ML theory, Mathematics, or Physics.
- Experience with Bayesian ML models (e.g., Gaussian Processes, Bayesian Neural Networks).
- Practical experience with Trust Region methods (PPO) and RL from Human Feedback (RLHF).
- Proven ability in debugging and setting up automated testing frameworks.
What We Do
Resaro was founded on the belief that every leap in AI innovation demands safeguards. Our mission is to enable an AI market worthy of trust. With co-headquarters in Singapore and Germany, we are an independent, third-party AI assurance provider that tests and evaluates mission-critical AI systems globally. We bring our breadth of domain expertise and networks to offer proprietary testing tools and protocols to help our customers shift from blind trust to informed trust. We are a member of the KI Park in Germany, and a premier member of Singapore IMDA’s AI Verify Foundation, and are committed to enhancing open-source tools that are globally interoperable as an enabler of trust in AI.
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