Senior Machine Learning Engineer
Cssmerge · San Francisco, CA
About this role
**Who we are** 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, far less efficient, and far more constrained. We’re changing that. Atoms builds **Physical AI**: real-world robots for the industries that move civilization forward, starting with **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, more scalable, and more 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. If you want to work on hard problems with real-world impact, join us. --- **What you’ll do** A visionary **Machine Learning Engineer** to join our founding team and help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We’re actively hiring across three core specialized subcategories: **AI Research**, **Post-Training Optimization**, and **Data Engineering**. - Research and develop cutting-edge **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, enabling re-simulation of events and queries about 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 --- **What we’re looking for** - 4+ years of non-internship professional MLE experience - Deep expertise 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** (e.g., Cameras, LiDAR, Radar)
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