Atoms is building Physical AI—real-world robots for critical industries like food, mining, and transport. We aim to make complex physical operations more reliable, scalable, and productive through deep integration of hardware, software, AI, operations, manufacturing, and real estate. Our team consists of roboticists, engineers, operators, and builders dedicated to transforming physical systems and creating lasting impact.

About the Position

Introduction

Atoms is building the machines that power the next era of progress. Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, less efficient, and more constrained. We’re changing that.

Atoms builds Physical AI—real-world robots for industries like food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, scalable, and productive.

This work requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don’t just build machines in a lab; we deploy them into real environments, operate them, learn from them, and improve them until they work at scale. We are roboticists, engineers, operators, and builders. We believe the next great technology companies will transform not only information but the physical systems that shape everyday life.

If you want to work on hard problems with real-world impact, join us. We are currently seeking a visionary Machine Learning Engineer to bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively hiring across three core specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.

Responsibilities

AI Researcher (World Models & VLA)

Research and develop cutting-edge Reinforcement Learning (RL) and distillation techniques for trajectory planning.
Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions.
Design and deploy end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation.
Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory.
Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases.
Partner with validation and QA teams to run model releases through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks.

Post-Training & Optimization

Own the post-training lifecycle by distilling, quantizing, and optimizing massive models to run with low latency on vehicle edge hardware.
Profile real-time inference pipelines to identify and eliminate CPU, GPU, and memory bandwidth bottlenecks on the vehicle.
Work with low-level hardware, electrical, and firmware teams to iterate on custom carrier boards, sensor interfaces, and GPUs on edge devices.
Benchmark and deploy models utilizing hardware-accelerated runtimes (e.g., TensorRT, CUDA) to minimize inference times under strict constraints.

Data & Long-Tail Scenarios

Architect automated pipelines to ingest, filter, and identify rare, high-value, and long-tail scenarios out of multi-petabyte multi-sensor datasets.
Target and extract complex structural corner cases from real-world driving logs to continuously feed, challenge, and improve our end-to-end behavior models.
Iterate closely with QA, testing, and simulation teams to transform ambiguous real-world anomalies into concrete data blocks for simulation testing.
Implement programmatic data curation, active learning strategies, and statistical quality metrics to optimize the signal-to-noise ratio of our training pipelines.

Requirements

4+ years of non-internship professional Machine Learning Engineering (MLE) experience.
For AI Researcher: Deep expertise in applying AI Transformers to robotics, physical actuation, or spatial-temporal data. Proven track record designing or training multimodal systems, large-scale VLA models, or generative Diffusion models. Strong background in Sensor Fusion, combining inputs from Cameras, LiDAR, and Radar. Fluency in PyTorch or JAX for training large-scale models. Proficiency in Python and familiarity with C++.
For Post-Training & Optimization: Strong background in machine learning engineering with a focus on model optimization, distillation, and deployment. Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. Deep understanding of profiling tools and debugging resource constraints across CPU/GPU boundaries. Experience with modern deep learning frameworks (PyTorch or JAX) and runtime compilation. Robust programming skills in Python and C++.
For Data & Long-Tail Scenarios: Professional experience building data curation pipelines, active learning workflows, or data mining architectures for massive physical datasets. Strong familiarity with robotics data structures and spatial frameworks, including Birds-Eye-View (BEV) or spatial tokenization. Experience processing and structuring raw data from Cameras, LiDAR, and Radar. Expert-level proficiency in Python, data engineering frameworks, and PyTorch/JAX. Exceptional ability to navigate, structure, and derive signal from highly ambiguous, messy, or undefined real-world data distributions.

Nice to Have

For AI Researcher: Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization.
For Post-Training & Optimization: Familiarity with low-level camera/sensor interfaces and robotics hardware.