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MetAntz in Bengaluru seeks experienced software engineers to build a production-grade robotics stack, turning capable robots into reliable products. You will own components across perception, control, cloud services, and analytics, collaborating with ML teams to push models to edge hardware and industrial environments.
You’ll work on ROS-based integrations, agentic frameworks, and scalable data pipelines, contributing to a fast-growing foundation-model robotics startup in India.
A high-growth foundation model startup headquartered in San Jose, California, operating at the intersection of two major platform shifts: foundation models and robotics.
A high-growth foundation model startup headquartered in San Jose, California, operating at the intersection of two major platform shifts: foundation models and robotics. The company is building the Claude Code for Physical AI: a robotic foundation model plus a physical agentic framework for reliable, long-horizon operations in industrial environments. It delivers semi-humanoid robots as a workforce for industrial applications. Their Robots combine a robotic foundation model, force‑aware manipulation, long‑horizon reasoning, step‑by‑step execution verified against real operating procedures, and human‑in‑the‑loop autonomy. The robot plans the job, executes each step, checks their work, and when it isn’t certain it doesn't guess—it escalates.
The team includes experienced executives and AI leaders from leading global technology companies across robotics, artificial intelligence, semiconductors, automotive, and enterprise technology.
You’ll be among the first engineers in the India team of a Silicon Valley deep tech startup. You’ll work on the same problems, with the same bar, as our team in San Jose. The India team is a core part of how we build models, data, and infrastructure, and you’ll own real pieces of the stack from your first quarter. Here you’ll own significant components, work across the whole stack, contribute at the cutting edge, see your code move a robot, and grow as fast as the company does. The physical AI field has shown broad competence across many tasks, and almost never 99.9% reliability on even one. That gap is the company. Closing it takes models that learn from demonstration and improve through practice, a data flywheel that turns every failure into training signal, and software that runs on real robots in real industrial environments.
You’ll build the software that turns a capable robot into a product customers rely on, from building the Claude code like agentic harness, the cloud services and data infrastructure down to the robot software itself.