Embodied AI Researcher: World Models & Systems

AIT Community; San Carlos (CA); Hybrid; USD 250,000 - 350,000; Full time

Benefits offered by this job

Salary range $250k–$350k

Health Insurance

401(k) match

PTO & holidays

Job summary

1X World Model Lab in the United States is seeking a research-oriented ML engineer to advance embodied AI by building scalable systems, data engines, and experimental pipelines. You will operate across model, data, infrastructure, and evaluation pillars to drive real-world robot learning.

The role emphasizes translating research into production capabilities, optimizing training throughput, and deploying improvements to robot hardware, with a focus on reproducible results and measurable impact.

Qualifications

• Strong Python and PyTorch with experience in large-scale codebases and data tooling and visualization.
• Experience in at least one pillar of AI: model and data, data infrastructure, ML infrastructure, or evaluation protocols.
• Degree in Computer Science, Machine Learning, or a related field; graduate-level education or equivalent research experience preferred.
• Track record of impact: published research, deployed production in modern AI systems, or infrastructure that accelerated a team.

Responsibilities

• Advance AI by building systems, infrastructure, and data engines for robot learning from experience.
• Ship research to production by deploying capabilities on robot hardware and measuring impact.
• Contribute to a learning flywheel where more robot experience leads to better models and deployments.

Skills

Python

PyTorch

Large-scale codebases

Data tooling

Visualization

Education

Degree in Computer Science, ML, or related field

Tools

TorchTitan

DeepSpeed

FSDP/ZeRO

ETL pipelines

CUDA

Job description

1X World Model Lab in the United States is seeking a research-oriented ML engineer to advance embodied AI by building scalable systems, data engines, and experimental pipelines. You will operate across model, data, infrastructure, and evaluation pillars to drive real-world robot learning.

The role emphasizes translating research into production capabilities, optimizing training throughput, and deploying improvements to robot hardware, with a focus on reproducible results and measurable impact.