AI Researcher (World Models & Embodied AI) — San Carlos, CA

Role: AI Researcher (World Models & Embodied AI)

Location: San Carlos, CA (Onsite)

Employment Type: Full-Time

About the Opportunity

A well-funded AI and robotics company is seeking an AI Researcher to help build next-generation embodied intelligence systems. This role sits at the intersection of large-scale AI, multimodal foundation models, data infrastructure, distributed training, and real-world robotics deployment.

You will contribute to the development of advanced AI systems capable of learning from real-world interactions and unstructured environments. The position offers the opportunity to work on cutting-edge research while directly influencing the deployment of intelligent robotic systems.

The Role

As an AI Researcher, you will help develop the models, infrastructure, data systems, and evaluation frameworks that drive intelligent robotic behavior.

The ideal candidate combines strong machine learning research capabilities with hands-on engineering experience and is comfortable building both experimental and production-grade systems.

This position offers flexibility to contribute across multiple areas including:

• World Models & Foundation Models

• Data Infrastructure & Tooling

• Machine Learning Infrastructure

• Evaluation Systems & Benchmarking

Key Responsibilities

Model & Data

• Develop multimodal generative models capable of learning from large-scale real-world data.

• Advance model architectures, tokenization strategies, and training methodologies.

• Improve robotic prediction, planning, and decision-making capabilities.

Data Infrastructure

• Design and scale data pipelines supporting visual, sensor, simulation, and robotic datasets.

• Build tooling for large-scale data curation, annotation, validation, and processing.

• Support training on web-scale, synthetic, and on-policy robotic data.

Machine Learning Infrastructure

• Improve distributed training throughput and model efficiency.

• Optimize GPU utilization and large-scale model training workloads.

• Enhance inference speed and model deployment performance.

Evaluation & Benchmarking

• Develop evaluation frameworks connecting model performance to real-world outcomes.

• Build automated benchmarking and model ranking systems.

• Improve confidence in research-to-production deployment decisions.

Cross-Functional Impact

• Collaborate with AI, software, infrastructure, and robotics engineering teams.

• Translate research breakthroughs into deployable capabilities.

• Drive improvements in research velocity and operational efficiency.

Required Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Robotics, or a related discipline.

• Strong programming experience with Python.

• Advanced experience using PyTorch or equivalent deep learning frameworks.

• Experience working in large-scale machine learning codebases.

• Demonstrated expertise in at least one of the following:

• Foundation Models

• Data Infrastructure

• Machine Learning Infrastructure

• Evaluation Systems

• Proven track record of impact through:

• Research publications

• Production AI deployments

• Large-scale ML infrastructure development

• Significant open-source contributions

Preferred Qualifications

• Experience with multimodal foundation models.

• Experience with:

• World Models

• Generative AI

• Diffusion Models

• Transformer Architectures

• Autoregressive Models

• Experience scaling distributed training systems.

• Familiarity with:

• DeepSpeed

• FSDP

• ZeRO

• TorchTitan

• Experience with large-scale data engineering and ETL workflows.

• Experience optimizing inference systems using:

• CUDA

• Triton

• TensorRT

• Quantization techniques (INT8, FP8, PTQ, QAT)

• Prior experience in robotics, autonomy, embodied AI, computer vision, or large-scale ML systems is highly desirable.

What You'll Bring

• Strong ownership mentality and ability to build systems from scratch.

• Ability to identify bottlenecks across the entire ML stack.

• Research depth combined with production engineering rigor.

• Experience designing systems that scale significantly over time.

• High degree of autonomy and bias toward execution.

Benefits

• Meaningful Equity Participation

• Medical, Dental & Vision Coverage

• 401(k) Program with Company Match

• Paid Time Off & Company Holidays

• Life & Disability Insurance

• Collaborative Onsite Environment

• Opportunity to work alongside leading AI and robotics researchers