AI Researcher

Halodi Robotics; San Carlos (CA); On-site; USD 250,000 - 350,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

401(k) with company match

Paid time off and holidays

Job summary

Halodi Robotics in San Carlos, CA, seeks an AI Researcher to advance embodied intelligence and build innovative AI systems. You will join a team focused on training robots to learn from real-world experiences, developing multi-modal models, and optimizing data processing infrastructure.

Applicants should possess strong Python and PyTorch skills, a degree in Computer Science or similar, and experience in AI-related practices. The position offers a competitive salary range of $250,000 - $350,000 and benefits including health, dental, and vision insurance.

Qualifications

• Strong Python and PyTorch experience with data tooling.
• Demonstrated experience in AI model, data infrastructure, or evaluation protocols.
• Track record of impact in modern AI systems.

Responsibilities

• Build AI systems for robots to learn from experience.
• Develop large multi-modal world models.
• Own distributed training and inference systems.

Skills

Python

PyTorch

Large-scale codebases

Data tooling and visualization

Multi-modal generative models

Inference optimization techniques

Education

Degree in Computer Science or Machine Learning

Graduate-level education preferred

Tools

TorchTitan

DeepSpeed

TensorRT

Job description

Job description

AI Researcher

San Carlos, CA (on-site, remote)

About the Lab

The 1X World Model Lab is an embodied AI research organization focusing on pretraining foundation models to accelerate the emergence of embodied intelligence. As the lab grows, researchers contribute where they have the most leverage, and the problems worth solving span every layer of the stack.

The lab is founded on a simple thesis: robotics is not a fine‑tuning problem. To build truly general humanoids, we need to pretrain on the most important data from the very beginning.

Your Charter

Advance NEO's intelligence by building the AI systems, infrastructure, and data engines that enable the robot to learn from experience and become increasingly capable in real‑world environments.

The key pillars of AI are:

Model and Data

Build large multi-modal generative world models that learn from robot experience, spanning model architecture, tokenization, and large-scale training and data processing. Advance the robot's ability to predict, plan, and act in unstructured environments.

Data Infrastructure and Tooling

Design and operate the data engine that enables training on all visual and robot data. From web‑scale media to egocentric and synthetic data, and most importantly, on‑policy NEO data, building large‑scale data infrastructure that enables annotation and curation at scale are crucial to scale up World Model training.

ML Infrastructure

Own the distributed training and inference systems that keep GPUs fully utilized. Increase the throughput during training, and speed of inference, to supercharge the model’s ability in the lab and in the world.

Evaluations

Build the evaluation infrastructure that connects pre‑training metrics to real‑world robot performance: benchmarks, evals frameworks, model ranking systems, and the tooling that lets the team iterate on architectures with confidence that lab results predict what happens in the real physical world.

Key Outcomes

• Advance robot capabilities through research, scaling data pipelines, optimizing training and inference throughput, or building evaluations that make lab results predictive of field performance
• Build infrastructure that multiplies team research velocity: pipelines that are faster, evaluations that are more predictive, training systems that are more efficient, or tooling that eliminates manual work across the lab
• Ship research to production: own the path from experimental result to deploy capability on robot hardware, and measure impact by what NEO can do, not just what the model achieves on benchmarks
• Contribute to a learning flywheel where more robot experience leads to better models, better models enable more capable robots, and more capable robots generate richer experience

Key Competencies

• 0 to 1 mentality excited to build systems from scratch that can efficiently ingest hundreds of millions of hours of videos, and excited to work through the tough and gritty aspects of engineering
• Full-stack ML thinker understanding the path from raw robot data to trained model to deployed policy, and can identify and address bottlenecks at any layer of that stack: data quality, training efficiency, model architecture, or inference performance
• Research depth plus engineering rigor conducting frontier research and builds systems others depend on; doesn't treat production engineering as someone else's job, and pushes work past promising training curves to deployed capabilities
• Scale-first mindset believing scale is foundational to capable humanoid robotics; designs systems with 10x and 100x growth in mind, and actively pushes to remove whatever is currently the binding constraint on model improvement
• Fast and high-agency contributor picking up new domains and codebases quickly, identifies the highest-leverage contribution, and makes meaningful progress without waiting for a detailed spec

Job requirements

Minimum Requirements

• Strong Python and PyTorch (or equivalent deep learning framework), with experience in large-scale codebases and data tooling and visualization
• Demonstrated experience in at least one area of the four pillars 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 strongly preferred
• Track record of impact: published research, deployed production in modern AI systems, or infrastructure that measurably accelerated a team's work

Preferred Skills

• Experience with distributed training frameworks (TorchTitan, DeepSpeed, FSDP/ZeRO) and/or large-scale data processing pipeline and ETL systems spanning on-device, on-premise, and cloud infrastructure
• Experience with multi-modal generative models, world models, diffusion models, or autoregressive architectures
• Experience with inference optimization techniques: quantization (PTQ, QAT, INT8/FP8), CUDA/Triton kernel development, or serving systems (TensorRT or equivalent)

Benefits & Compensation

• Salary Range: $250,000 - $350,000 + competitive equity
• Health, dental, and vision insurance
• 401(k) with company match
• Paid time off and holidays

Equal Opportunity Employer

1X is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, citizenship, age, marital status, medical condition, genetic information, disability, military or veteran status, or any other characteristic protected under applicable federal, state, or local law.