The IC5 Applied Scientist for Robotics, Perception, and Embodied AI will serve as a senior technical leader responsible for defining, prototyping, and delivering AI capabilities for commercially viable robotic systems. This role partners across science, software engineering, product, and hardware teams to identify high-impact opportunities, translate ambiguous product needs into research and engineering roadmaps, and guide end-to-end solution development from data collection and experimentation through production deployment. The scientist will lead applied research in multi-sensor fusion, real-time signals, perception, multimodal reasoning,
reinforcement learning, and action-conditioned planning, with a strong bias for hands-on execution and full-stack delivery.

Key Responsibilities

Solution Identification and Strategy

• Partner with science, engineering, product, and hardware leaders to identify strategic product needs where robotics, perception, and embodied AI can create measurable customer and business impact.

• Evaluate academic literature, industry benchmarks, robotics platforms, and commercially viable robot APIs to assess feasibility, technical difficulty, and delivery risks.

• Break down ambiguous robotics and AI problems into clear research plans, model architectures, data requirements, evaluation criteria, and production milestones.

• Set science quality standards for applied robotics workstreams, including perception accuracy, latency, robustness, safety, reliability, and operational feedback metrics.

• Prioritize solutions using the scientific process, including modeling approaches, evaluation techniques, data collection strategies, and risk/reward tradeoffs.

Applied Research and Model Development

• Lead the design and execution of research programs and POCs for sensors, multi-sensor fusion, real-time signal processing, and perception systems.

• Develop and guide approaches for object detection, tracking, activity recognition, scene understanding, and related perception capabilities in real-world environments.

• Advance multimodal AI systems that combine signals such as camera, depth, lidar, audio, telemetry, proprioception, and task context.

• Apply reinforcement learning, action-conditioned planning, decision-making, and reasoning methods to enable robot behaviors that are robust, measurable, and product-relevant.

• Define dataset strategy, data quality criteria, labeling approaches, simulation or synthetic data opportunities, and evaluation protocols for robotics and embodied AI use cases.

• Guide model training, fine-tuning, optimization, inference design, and compute/latency tradeoffs for real-time or near-real-time deployment scenarios.

Solution Delivery and Production Integration

• Lead full-stack execution across experimentation, data pipelines, model development, evaluation, deployment integration, and production monitoring.

• Partner closely with software engineering, machine learning engineering, hardware teams, and product stakeholders to integrate AI capabilities into robotic systems and services.

• Evaluate and review high-complexity code, establish best practices for repositories, version control, code review, documentation, testing, and delivery readiness.

• Define operational metrics and user feedback loops to assess delivered solutions in production and inform future technical strategy.

• Serve as an escalation point for complex robotics, AI, perception, and systems integration issues, driving root-cause analysis and durable solutions.

Research Leadership and Influence

• Demonstrate thought leadership in at least one business-critical area such as robot perception, multimodal systems, sensor fusion, reinforcement learning, or embodied AI.

• Translate research insights into clear technical recommendations, patents, white papers, design documents, demos, or conference-quality publications where appropriate.

• Mentor and guide scientists and engineers, raising the bar for applied research rigor, experimentation quality, and production readiness.

• Establish productive collaborations with internal teams, external research groups, academic partners, or commercial robotics ecosystem partners where relevant.

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