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Atoms is hiring a 12-week onsite ML intern in San Francisco to help build AI for autonomous transport robots: prototyping trajectory-planning models, fusing camera/LiDAR/radar data, building simulation and world models, and optimizing low-latency inference on vehicle edge hardware using PyTorch/JAX and Python.
Build and deploy AI models (LLMs, GNNs, deep learning) for robotics and logistics in food and other industries, turning physical operations into scalable, automated systems.
Own full-cycle technical recruiting at Atoms for specialized AI/ML, robotics, perception, autonomy, and ML infrastructure roles — sourcing passive candidates, partnering with engineering leaders, and running the hiring process onsite five days a week from the San Francisco office. Core skills: technical sourcing, candidate evaluation, and pipeline management.
Senior Staff ML Engineer who defines and builds the perception stack for Atoms' Physical AI robots: developing multimodal sensor-fusion and deep learning systems (camera, LiDAR, radar, audio) that turn raw sensor data into world representations for downstream planning and action models. Hands-on Python/C++ role with major architectural influence, onsite in San Francisco.
Senior Staff ML Engineer at Atoms (a Physical AI robotics company) to serve as a foundational technical leader building world models and foundation models that learn representations of physical environments from large-scale multimodal data (video, sensors, robot state). Deeply hands-on role in Python/ML frameworks covering research, architecture, training, and deployment for robotics, onsite in Sa
Staff-level machine learning engineer at Atoms, a physical-AI robotics company, defining the architecture for action models that let real-world robots reason, plan, and act. Day-to-day: research and build learned policies and multimodal (vision-language-action) models, train on real/simulated data, and set research direction.
Build and deploy AI models for autonomous transport robots, integrating perception, planning, and edge optimization in real-world industrial settings.
Build and deploy AI models for autonomous transport robots, integrating perception, planning, and edge deployment in real-world industrial settings.
Build and maintain ML infrastructure for data scientists and engineers, including classical ML and GenAI tooling, on Azure and GCP using Python/Java and Kubernetes.
Build and scale the distributed GPU training infrastructure and MLOps platform that powers Atoms’ autonomous transport models, using Kubernetes, Ray, and MLflow.
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