Staff Machine Learning Engineer

Atoms is building Physical AI— real-world robots for industries like food, mining, and transport. Our mission is to make complex physical operations more reliable, scalable, and productive by designing systems that understand, predict, and control the real world with precision. We are a team of roboticists, engineers, operators, and builders committed to transforming physical systems that shape everyday life. We deploy and operate our machines in real environments, constantly learning and improving to achieve scale. Atoms fosters an environment where team members can do their best work and grow, grounded in a shared purpose and commitment to real-world impact.

Introduction

Atoms is seeking a visionary Staff Machine Learning Engineer to join our founding team. This role focuses on bridging high-level AI research with real-world physical actuation for next-generation autonomous transport platforms. We are hiring across three 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 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 for event re-simulation and trajectory alteration queries.
• Ensure autonomous driving models adapt seamlessly to novel urban environments and edge cases.
• Partner with validation and QA teams to run rigorous simulated scenarios, detect regressions, and identify performance bottlenecks.

Post-Training & Optimization

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

Data & Long-Tail Scenarios

• Architect automated pipelines for ingesting, filtering, and identifying rare and high-value long-tail scenarios from multi-petabyte multi-sensor datasets.
• Extract complex structural corner cases from real-world driving logs to continuously feed and improve end-to-end behavior models.
• Iterate 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 training pipeline signal-to-noise ratio.

Requirements

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

Nice to Have

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

Benefits

• Medical, Dental, Vision, Disability, and Life Insurance
• Flexible Spending Account / Health Savings Account Options
• 401(k)
• Equity
• Sick Time, Unlimited Flexible Time Off, and Paid Holidays
• Paid Parental Leave
• Pre-Tax Commuter Benefit Plan
• Team lunch in our SoMa office every Tuesday and Thursday

About Company

Atoms is building Physical AI— real-world robots for industries like food, mining, and transport. Our mission is to make complex physical operations more reliable, scalable, and productive by designing systems that understand, predict, and control the real world with precision. We are a team of roboticists, engineers, operators, and builders committed to transforming physical systems that shape everyday life. We deploy and operate our machines in real environments, constantly learning and improving to achieve scale. Atoms fosters an environment where team members can do their best work and grow, grounded in a shared purpose and commitment to real-world impact.

How to Apply

Atoms accepts applications on an ongoing basis. Please apply through the provided link.