Research Engineer

Pantograph; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Pantograph is training general models that start by watching internet-scale video and end up on robots. We think the path to capable robots runs through general intelligence rather than narrow, robot-specific skills.

We're scaling simple methods across video, real-world data, and our fleet of durable robots. You’ll work across the boundary between research and engineering: implementing new ideas, scaling experiments across large GPU clusters, building the systems that let us iterate quickly, and

Qualifications

• Experience training models on large GPU clusters.
• Proficiency with Kubernetes and distributed systems.
• Ability to scale experiments across multiple clusters and datasets.
• Track record building production-like ML systems with observable metrics.

Responsibilities

• Design scalable training pipelines and multimodal models.
• Scale experiments across large GPU clusters and data stores.
• Build systems enabling rapid iteration between research and production.
• Analyze failures, improve observability, and drive system reliability.
• Collaborate across research and engineering to deploy capabilities.

Skills

Kubernetes

Distributed systems

Large-scale data processing

Multimodal model training

Experimentation & debugging

Research-to-product bridge

Python / ML engineering

Tools

JAX

CUDA kernels

Linux kernel programming

GPU clusters

TensorFlow / PyTorch

Job description

Pantograph is training general models that start by watching internet-scale video and end up on robots. We think the path to capable robots runs through general intelligence rather than narrow, robot-specific skills. We're scaling simple methods across video games, real-world video, and our own fleet of affordable, durable robots.

We're looking for a research engineer to help us train increasingly capable models across enormous and diverse datasets.

You’ll work across the boundary between research and engineering: implementing new ideas, scaling experiments across large GPU clusters, building the systems that let us iterate quickly, and figuring out why things aren’t working. The work spans large-scale model training, multimodal representation learning, reinforcement learning, data processing, evaluation, and the infrastructure required to support all of it.

You might be a good fit if you:

• Have trained models across large GPU clusters and are comfortable working with Kubernetes

• Have built or operated complex distributed systems

• Have worked with multi-terabyte or multi-petabyte datasets

• Are comfortable with large-scale data processing tools

• Care deeply about observability and collect enough metrics to understand what every part of a system is doing

• Are comfortable moving between research code and production-quality systems

• Like running experiments, getting surprising results, and digging in until you understand why

• Move quickly and reach for simple approaches before complicated ones

Nice to have:

• Experience with JAX

• Experience writing CUDA kernels or otherwise optimizing GPU workloads

• Low-level Linux or kernel programming experience

• Experience with large-scale video or multimodal datasets

• Experience building training or evaluation infrastructure

• Experience with distributed training

• Experience deploying models into real-world systems, especially robotics

We care much more about what you've built than any specific credential. We're a small, fast-moving team working together in person in San Francisco. If you're excited about architecting novel systems at unprecedented scale, we'd love to talk.