Sr. Applied Scientist, Foundation Model, Industrial Robotics , Industrial Robotics Group

Job  |  Services LLC

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We’re building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world‑class teams pushing the boundaries of what’s possible in robotic manipulation, locomotion, and human‑robot interaction.

This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models.

We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence.

We are pioneering the development of robotics foundation models that:

• Enable unprecedented generalization across diverse tasks
• Integrate multi‑modal learning capabilities (visual, tactile, linguistic)
• Accelerate skill acquisition through demonstration learning
• Enhance robotic perception and environmental understanding
• Streamline development processes through reusable capabilities

The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that is revolutionizing how robots learn, adapt, and interact with their environment.

• Lead technical initiatives for foundation‑model capabilities (e.g., visuomotor / VLA / video‑action world‑model‑action policies), from problem definition through validated model deliverables.
• Own model readiness for our embodiment class: drive adaptation, fine‑tuning, and optimization (latency/throughput/robustness), and define success criteria that downstream teams can build on.
• Establish and evolve capability evaluation: define benchmark strategy, metrics, and profiling methodology to quantify performance, generalization, and failure modes; ensure evaluations drive clear roadmap decisions.
• Drive the data + training strategy needed to close key capability gaps, including data requirements, collection/curation standards, dataset quality/provenance, and repeatable training recipes (sim + real).
• Invent and validate new methods when leveraging SOTA is insufficient—new training schemes, model components, supervision signals, or sim↔real techniques—backed by strong empirical evidence.
• Influence cross‑team technical decisions by collaborating with controls/WBC, hardware, and product teams on interfaces, constraints, and integration plans; communicate results via design docs and technical reviews.
• Mentor and raise the bar: guide junior scientists/engineers, set best practices for experimentation and code quality, and drive a culture of rigor and reproducibility.

• PhD, or Master’s degree and 6+ years of applied research experience.
• Hands‑on deep learning expertise, with demonstrated impact in one or more: multimodal modeling, imitation learning/RL for robotics, vision‑language/video models, sim2real/real2sim.
• Experience leading technical projects end‑to‑end (problem framing → execution → measurable outcomes), with strong experimental rigor and reproducible baselines.
• Software engineering skills (Python + C++/Java), ability to build reliable training/evaluation infrastructure and production‑quality research code.
• Demonstrated technical contributions (publications/patents/open‑source or substantial internal impact) and ability to communicate technical direction clearly.

• Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc.
• Experience with large‑scale distributed systems such as Hadoop, Spark, etc.
• Experience driving robotics foundation models…