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Job Summary:
Atoms is building the machines that power the next era of progress. They are seeking a visionary Machine Learning Engineer to bridge the gap between high-level AI research and real-world physical actuation for next-generation autonomous transport platforms.
Responsibilities:
• 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, 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.
• 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.
• 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.
Qualifications:
Required:
• 4+ years of non-internship professional MLE experience.
• 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++.
• 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++.
• 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.
Preferred:
• Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization is highly preferred.
• Familiarity with low-level camera/sensor interfaces and robotics hardware is a significant plus.
Company:
Atoms is a robotics startup that develops industrial robotics and physical AI systems to automate tasks across various industries. Founded in 2016, the company is headquartered in Los Angeles, USA, with a team of 1001-5000 employees. The company is currently Late Stage.