About the Company
Our goal at P-1 AI is to develop an artificial general engineering intelligence—and eventually superintelligence—that can help the human species design physical systems more efficiently and at unprecedented levels of complexity. Going beyond existing foundation models, our autonomous AI agent learns from synthetic training data and real-world feedback and reasons over an internal multi-physics representation of a product design that encompasses both geometry and function. We are a world-class team of AI researchers, engineers, and top industry executives backed by some of the best investors in Silicon Valley and beyond.
About the Role
In this role, you’ll be at the frontier of generative AI and foundation models working on building augmenting these models with reasoning capability needed for the design and engineering of physical systems. Working with a small, tightly-knit team, you’ll be principally responsible for building generative AI models that will be the core of a general engineering intelligence. You'll tackle many challenges, such as developing tasks to train large language models on engineering data, training LLMs and multimodal foundation models, enabling reasoning capability in these models through training and augmentation, developing an evaluation pipeline to measure progress of the technology on engineering tasks of varying complexity, and interfacing with simulation engineering and domain-experts to deploy this technology on real-world problems. If you're passionate about generative AI, enjoy being on the frontier of applied AI, and love tackling complex engineering challenges, this role offers you the perfect platform to make a significant impact while building cutting-edge skills alongside some of the world’s top experts.
Responsibilities
• Develop training tasks for a generative model capable of engineering physical systems using data curated by domain experts.
• Finetune open-source LLMs such as Llama and Mixtral with engineering data and tasks.
• Develop a training and evaluation pipeline on multi-node cloud platforms.
• Implement augmentation methods and tool calling to integrate quantitative reasoning capability into LLMs needed for engineering tasks.
• Develop training pipeline for surrogate models that can include multimodal foundation models such as those over pointcloud.
• Integrate the AI/ML pipeline with data curation pipeline using specialized engineering software for simulation and design of physical systems.
• Implement best AI/ML practices to ensure proper storage, formatting, and tool interoperability.
Skills
• Proven experience (2+ years) working on generative AI models such as Large Language Models.
• Experience with PyTorch and LLM ecosystem and libraries such as Huggingface libraries, DeepSpeed, and wandb.
• Demonstrated ability to quickly learn and work with new foundation models.
• Experience with version control systems (e.g.: Git), collaborative software development practices, and continuous integration / continuous deployment (CI/CD) systems.
• Proficiency in Python.
• Strong problem-solving skills and ability to work in a fast-paced, unstructured startup environment.
Preferred Skills
• Experience with training diffusion models or graph neural networks or Physics-informed neural networks.
• Familiarity with functional programming languages and/or physical/mechanical modeling platforms such as Simulink/Modelica.
• Familiarity with data curation and/or MLOps tools.
Representative projects
• Finetuning open LLMs on a set of modelica/simulink designs and their performances.
• Building an evaluation pipeline that can test the generalization of a finetuned LLM on prompts to create new designs.
• Analyzing gaps in a generative AI model and identifying the nature of training data needed to improve model generalization and performance.
• Training physics-inspired neural network models as surrogates to predict physical properties of an engineering design.
• Augmenting LLMs with retrieval over well-curated knowledge base and interface to simulation tools to improve accuracy of the models.
Location: Anywhere
Posted: Aug. 16, 2024, 2:34 p.m.
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