Senior Machine Learning Engineer, Data For Embodied AI (AI Engineering)

TL;DR

Senior Machine Learning Engineer (AI Engineering): Building and scaling next-generation world model architectures and bridging them into high-throughput training infrastructure, enabling synthetic data and simulation. Focus on designing systems to acquire, process, and curate multimodal data at scale and turning raw experience into high-quality datasets.

Location: London, United Kingdom

Company

hirify.global is the leading developer of Embodied AI technology.

What you will do

• Design and implement large-scale data acquisition, processing, and curation pipelines for high-quality datasets.
• Improve dataset quality and utility through sophisticated data analysis, debugging, and experimentation.
• Develop and scale multimodal data pipelines for ingestion, preprocessing, filtering, annotation, and storage across video, LiDAR, and telemetry modalities.
• Run systematic experiments on data ablations and composition to assess their impact on model training dynamics.
• Collaborate with ML researchers and platform engineers to ensure datasets are fit for purpose and efficiently integrated into large-scale training workflows.
• Build internal tools and workflows for dataset auditing, visualization, and versioning to streamline iteration and reproducibility.

Requirements

• Experience in ML engineering, data engineering, or applied ML roles focused on large-scale data systems.
• Proven experience building and maintaining large-scale data pipelines for machine learning.
• Strong Python fundamentals and experience with modern ML and data frameworks (e.g. PyTorch, Ray, Dask, Spark).
• Solid understanding of multimodal data (video, lidar, sensor telemetry) and its challenges in large-scale training.
• Experience defining and tracking data quality metrics, conducting dataset analysis, and driving data-informed improvements in model performance.
• Demonstrated ability to work collaboratively with ML researchers, platform engineers, and product teams in a fast-paced, experimental environment.

Nice to have

• Exposure to large-scale storage, distributed training systems, or cloud compute environments (Azure, AWS, GCP).
• Experience designing high-throughput, distributed data pipelines.
• Familiarity with data versioning, lineage, and governance tools.
• Experience in AVs, robotics, simulation, or other embodied AI domains.
• Familiarity with foundation models, generative models, or simulation-based data pipelines.

Culture & Benefits

• Shape the future of embodied AI through data.
• Tackle data challenges at unprecedented scale.
• Collaborate with world-class talent.
• Make your mark on real-world autonomy.
• Work in a high-trust, high-autonomy environment.