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  "description": "<p><strong><span style=\"color:#5a5f72\">AI Researcher</span></strong></p><p><strong><span style=\"color:#5a5f72\">San Carlos, CA (on-site, remote)</span></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">About the Lab</span></strong></p><p><span style=\"color:#5a5f72\">The 1X World Model Lab is an embodied AI research organization focused on pretraining the 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.</span></p><p style=\"min-height: 1.7em;\"></p><p><span style=\"color:#5a5f72\">The lab is founded on a simple thesis:</span><strong><em><span style=\"color:#5a5f72\"> 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.</span></em></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Your Charter</span></strong></p><p><span style=\"color:#5a5f72\">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.&nbsp;</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">The key pillars of AI are:</span></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Model and Data</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: good tokens in = good tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Data Infrastructure and Tooling</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: more tokens in = more tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">ML Infrastructure</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: more tokens seen = better tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Evaluations</span></em></strong></p><p><span style=\"color:#5a5f72\">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 the real physical world. Simply: more tokens evaluated = better model performance!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Key Outcomes</span></strong></p><ul><li><p><span style=\"color:#5a5f72\">Advance robot capabilities through research, scaling data pipelines, optimizing training and inference throughput, or building evaluations that make lab results predictive of field performance</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Key Competencies</span></strong></p><ul><li><p><strong><span style=\"color:#5a5f72\">0 → 1 mentality </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Full-stack ML thinker </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Research depth plus engineering rigor </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Scale-first mindset </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Fast and high-agency contributor </span></strong><span style=\"color:#5a5f72\">picking up new domains and codebases quickly, identifies the highest-leverage contribution, and makes meaningful progress without waiting for a detailed spec</span></p></li></ul>",
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  "requirements": "<p><strong><span style=\"color:#7a7e8d\">Minimum Requirements</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">Strong Python and PyTorch (or equivalent deep learning framework), with experience in large-scale codebases and data tooling and visualization</span></p></li><li><p><span style=\"color:#7a7e8d\">Demonstrated experience in at least one area of the four pillars of AI: model and data, data infrastructure, ML infrastructure, or evaluation protocols</span></p></li><li><p><span style=\"color:#7a7e8d\">Degree in Computer Science, Machine Learning, or a related field; graduate-level education or equivalent research experience strongly preferred</span></p></li><li><p><span style=\"color:#7a7e8d\">Track record of impact: published research, deployed production in modern AI systems, or infrastructure that measurably accelerated a team's work</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Preferred Skills</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">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</span></p></li><li><p><span style=\"color:#7a7e8d\">Experience with multi-modal generative models, world models, diffusion models, or autoregressive architectures</span></p></li><li><p><span style=\"color:#7a7e8d\">Experience with inference optimization techniques: quantization (PTQ, QAT, INT8/FP8), CUDA/Triton kernel development, or serving systems (TensorRT or equivalent)</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Benefits &amp; Compensation</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">Salary Range: $250,000 - $350,000 + competitive equity</span></p></li><li><p><span style=\"color:#7a7e8d\">Health, dental, and vision insurance</span></p></li><li><p><span style=\"color:#7a7e8d\">401(k) with company match</span></p></li><li><p><span style=\"color:#7a7e8d\">Paid time off and holidays</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Equal Opportunity Employer</span></strong></p><p><span style=\"color:#7a7e8d\">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.</span></p>",
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      "description": "<p><strong><span style=\"color:#5a5f72\">AI Researcher</span></strong></p><p><strong><span style=\"color:#5a5f72\">San Carlos, CA (on-site, remote)</span></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">About the Lab</span></strong></p><p><span style=\"color:#5a5f72\">The 1X World Model Lab is an embodied AI research organization focused on pretraining the 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.</span></p><p style=\"min-height: 1.7em;\"></p><p><span style=\"color:#5a5f72\">The lab is founded on a simple thesis:</span><strong><em><span style=\"color:#5a5f72\"> 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.</span></em></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Your Charter</span></strong></p><p><span style=\"color:#5a5f72\">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.&nbsp;</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">The key pillars of AI are:</span></strong></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Model and Data</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: good tokens in = good tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Data Infrastructure and Tooling</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: more tokens in = more tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">ML Infrastructure</span></em></strong></p><p><span style=\"color:#5a5f72\">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. Simply: more tokens seen = better tokens out!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><em><span style=\"color:#5a5f72\">Evaluations</span></em></strong></p><p><span style=\"color:#5a5f72\">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 the real physical world. Simply: more tokens evaluated = better model performance!</span></p><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Key Outcomes</span></strong></p><ul><li><p><span style=\"color:#5a5f72\">Advance robot capabilities through research, scaling data pipelines, optimizing training and inference throughput, or building evaluations that make lab results predictive of field performance</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li><li><p><span style=\"color:#5a5f72\">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</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#5a5f72\">Key Competencies</span></strong></p><ul><li><p><strong><span style=\"color:#5a5f72\">0 → 1 mentality </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Full-stack ML thinker </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Research depth plus engineering rigor </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Scale-first mindset </span></strong><span style=\"color:#5a5f72\">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</span></p></li><li><p><strong><span style=\"color:#5a5f72\">Fast and high-agency contributor </span></strong><span style=\"color:#5a5f72\">picking up new domains and codebases quickly, identifies the highest-leverage contribution, and makes meaningful progress without waiting for a detailed spec</span></p></li></ul>",
      "requirements": "<p><strong><span style=\"color:#7a7e8d\">Minimum Requirements</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">Strong Python and PyTorch (or equivalent deep learning framework), with experience in large-scale codebases and data tooling and visualization</span></p></li><li><p><span style=\"color:#7a7e8d\">Demonstrated experience in at least one area of the four pillars of AI: model and data, data infrastructure, ML infrastructure, or evaluation protocols</span></p></li><li><p><span style=\"color:#7a7e8d\">Degree in Computer Science, Machine Learning, or a related field; graduate-level education or equivalent research experience strongly preferred</span></p></li><li><p><span style=\"color:#7a7e8d\">Track record of impact: published research, deployed production in modern AI systems, or infrastructure that measurably accelerated a team's work</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Preferred Skills</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">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</span></p></li><li><p><span style=\"color:#7a7e8d\">Experience with multi-modal generative models, world models, diffusion models, or autoregressive architectures</span></p></li><li><p><span style=\"color:#7a7e8d\">Experience with inference optimization techniques: quantization (PTQ, QAT, INT8/FP8), CUDA/Triton kernel development, or serving systems (TensorRT or equivalent)</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Benefits &amp; Compensation</span></strong></p><ul><li><p><span style=\"color:#7a7e8d\">Salary Range: $250,000 - $350,000 + competitive equity</span></p></li><li><p><span style=\"color:#7a7e8d\">Health, dental, and vision insurance</span></p></li><li><p><span style=\"color:#7a7e8d\">401(k) with company match</span></p></li><li><p><span style=\"color:#7a7e8d\">Paid time off and holidays</span></p></li></ul><p style=\"min-height: 1.7em;\"></p><p><strong><span style=\"color:#7a7e8d\">Equal Opportunity Employer</span></strong></p><p><span style=\"color:#7a7e8d\">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.</span></p>",
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