Machine Learning Engineer

Role overview This role owns the design and optimization of machine learning pipelines responsible for processing petabyte-scale datasets in the physical AI domain. It is a research-facing engineering position: the engineer partners closely with research teams to construct models that learn from real-world data, bridging data infrastructure and model development end to end.

Responsibilities - Architecting and tuning ML pipelines for petabyte-scale physical AI datasets. - Collaborating with research teams to align pipeline output with model requirements. - Building and maintaining the data backbone for training models on real-world inputs. - Driving performance, reliability, and cost improvements across the training data lifecycle.

Requirements - Proven track record designing ML pipelines for very large datasets. - Understanding of physical AI data characteristics and the demands of real-world learning. - Ability to work shoulder-to-shoulder with research scientists on model development.

Nice to have - Background in distributed training, large-scale data processing, or simulation-heavy environments. - Prior exposure to robotics, autonomy, or sensor-fusion AI systems.