Research Scientist

• Your focus will be defined by the intersection of ML research, robotics, and large-scale multimodal model training. Expect challenges across two main pillars, with the opportunity to specialize in Pretraining or Post-training:
• Foundation Models & Pretraining
• Design the Base Intelligence: Define model architectures (Transformer- and Diffusion-based), objectives, and training curricula across multimodal robotic data, turning raw deployment logs into generalizable capabilities.
• Master the Data: Develop scalable data mixtures and sampling strategies utilizing our massive offline repositories of vision, action, and state data.
• Push the Frontier: Run rigorous ablations to understand scaling laws, data quality effects, optimization dynamics, and large-model failure modes.
• Scale with Engineering: Collaborate closely with ML Infra to push cluster utilization and throughput, ensuring our algorithmic ideas translate to efficient distributed training.
• Adaptation, Post-Training & Real-World Evaluation
• Drive Downstream Adaptation: Explore fine-tuning recipes to make general models – our own as well as our partner’s models – useful, controllable, and safe in the real world using imitation and reinforcement learning, distillation, and curriculum learning.
• Improve Physical Robustness: Develop cutting-edge methods for improving real-world reliability, handling out-of-distribution edge cases, and steering robot behavior in mature factory environments.
• Build Benchmarks: Design evaluation frameworks and lightweight physical setups that measure actual robot performance and failure modes far beyond the limits of simulation.
• Close the Physical Loop: Analyze real-world evaluation results to guide the overarching research direction, seamlessly bridging the gap between foundation model outputs and physical-world outcomes.

• Contributing to Client's product strategy and helping define the future of their merchant service.
• Exploring complex, ambiguous problem spaces in partnership with the team to understand opportunities and chart a path forward.
• Influencing the team's approach to product development, shaping both the organization and its processes.
• Designing and running studies using a broad range of qualitative and quantitative methods — including interviews, contextual inquiry, diary studies, and survey design.
• Using AI-powered tools to accelerate synthesis, pattern recognition, and the delivery of insights at scale.
• Improving the experience of millions of Client's merchants every day.

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Research Scientist

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