> Markdown version of [/jobs/ext/599737-ai-researcher-world-model-labs](https://www.wearedevelopers.com/jobs/ext/599737-ai-researcher-world-model-labs). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Researcher - World Model Labs - **Company:** 1X, LLC - **Location:** San Carlos, CA, United States - **Salary:** $200,000.0 - $350,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Nvidia CUDA, Data Infrastructure, Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Linux Kernel, Machine Learning, Pytorch, Deep Learning, Generative AI, Information Technology, Machine Learning Operations, TensorRT - **Published:** June 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e7d5af62f4b67d5c ## About the Role Do you have experience in Tooling?, * 0 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, * 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) ## Related Videos - [Localized Open Models in Production: What Builders Need to Know](https://www.wearedevelopers.com/videos/100270-localized-open-models-in-production-what-builders-need-to-know) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)