AI Research Scientist Jobs

Design and build reinforcement learning environments that model real-world customer interaction workflows. Design RL agents that learn from these environments using real-world interaction data, rewards, and feedback loops. Define reward models and feedback loops using real-world signals such as outcomes and human feedback. Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning. Experiment with multi‑agent systems and simulation frameworks for complex coordination and decision‑making. Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale. You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend state‑of‑the‑art methods in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built by the company, interfacing with them, and evaluating their robustness in real‑world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds. As a Senior AI Research Scientist for Perception for Contact Rich Manipulation, you will lead the research and development of novel deep learning algorithms to enable robots to perform complex, contact‑rich manipulation tasks. Your responsibilities include exploring the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments, and creating models that integrate visual data to guide physical manipulation beyond simple grasping to sophisticated handling of diverse items. You will collaborate with a multidisciplinary team of engineers and researchers to translate cutting‑edge concepts into robust capabilities deployable on physical hardware for industrial applications. Drive the full life‑cycle of AI systems from conception through deployment, including building robust evaluation frameworks to measure and improve agent performance. Collaborate closely with engineering and product teams to integrate AI capabilities into the product experience. Stay at the forefront of AI by exploring the latest advancements in agents, evals, and applied AI research. Provide strategic direction and mentorship while contributing directly to prototyping, building, and iterating on AI systems. Build realistic consulting project environments by creating detailed project scenarios including industry context, financials, constraints, and incomplete information. Design structured consulting tasks for AI Agents by breaking projects into tasks that reflect real consulting work such as market sizing, due diligence, cost reduction, growth strategy, or operational diagnosis. Define evaluation criteria and quality standards by developing a grading approach, evaluation criteria, and golden solutions for each task to be used in training and calibrating an LLM‑based grading system that evaluates AI outputs at scale. This role is remote, individual‑contributor, project‑based, and focused on analytical design and evaluation. #J-18808-Ljbffr