Member of Technical Staff — ML Research, Multimodal

Kindredventures; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Kindredventures is building a Large Physics foundation Model to predict the future of physical systems and to identify actions to alter it. The team aims to design architectures and training recipes that learn from multimodal observations, including sparse sensors, point clouds, and hyperspectral data, at scales surpassing current frontier models.

We seek researchers who excel at problem-solving, rapid execution, and rapid learning across unfamiliar domains, with deep expertise in ML

Qualifications

• Strong grasp of machine learning fundamentals, with depth in at least one relevant domain (e.g. sequence or world models, computer vision, sensor fusion, generative modeling, physics-informed NNs)
• Experience training large-scale models and the ability to understand experimental results through careful analysis and ablation studies
• Familiarity with distributed training and the systems considerations of scaling models

Responsibilities

• Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data
• Solve core modeling problems unique to physical prediction: encoding heterogeneous and irregularly-sampled modalities, stable long-horizon rollouts, and probabilistic forecasting
• Run experiments and ablations that connect modeling and data decisions to predictive skill, including which data sources and mixtures most improve the model
• Work across the full ML stack — data, model, eval, and infrastructure — to take ideas from prototype to scaled training runs
• Stay up-to-date on research to bring new ideas to work

Skills

Machine learning fundamentals

World models

Sequence modeling

Computer vision

Sensor fusion

Generative modeling

Physics-informed neural networks

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for researchers who are excited to tackle unsolved problems. Predicting how physical systems evolve means learning from observations that language and vision models were not built for — sparse sensors, point clouds, hyperspectral imagery, physical fields — at a scale that dwarfs what is used to train even today's frontier LLMs. Your mission is to design the architectures and training recipes that turn these multimodal observations into a model that predicts the future of the physical world.

Responsibilities

• Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data
• Solve core modeling problems unique to physical prediction: encoding heterogeneous and irregularly-sampled modalities, stable long-horizon rollouts, and probabilistic forecasting
• Run experiments and ablations that connect modeling and data decisions to predictive skill, including which data sources and mixtures most improve the model
• Work across the full ML stack — data, model, eval, and infrastructure — to take ideas from prototype to scaled training runs
• Stay up-to-date on research to bring new ideas to work

What we’re looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

• Strong grasp of machine learning fundamentals, with depth in at least one relevant domain (e.g. sequence or world models, computer vision, sensor fusion, generative modeling, physics-informed NNs)
• Experience training large-scale models and the ability to understand experimental results through careful analysis and ablation studies
• Familiarity with distributed training and the systems considerations of scaling models