Senior Machine Learning Engineer

Atoms is at the forefront of building Physical AI—real-world robots—for critical industries such as food, mining, and transport. Our mission is to enhance the intelligence, efficiency, and capabilities of the physical world. We achieve this by creating systems that accurately understand, predict, and control real-world operations, transforming complex physical tasks into reliable, scalable, and productive processes. As a team of roboticists, engineers, operators, and builders, we are dedicated to solving hard problems with real-world impact, pushing the boundaries of what's known, and building what doesn't yet exist. We prioritize a collaborative environment where continuous innovation and growth are fostered. Our operations are based in our San Francisco office, where all office-based teams work five days a week.

Introduction

Atoms is building the machines that power the next era of progress, focusing on Physical AI for the physical world, including industries like food, mining, and transport. Our systems are designed to understand, predict, and control real-world operations with precision, making them more reliable, scalable, and productive. This role requires deep integration across hardware, software, AI, operations, manufacturing, and real estate, deploying machines into real environments and continuously improving them.

We are seeking a visionary Senior Machine Learning Engineer to join our founding team. This role will bridge high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively 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 to re-simulate events and query vehicle behavior.
• Ensure autonomous driving models adapt seamlessly to novel urban environments and edge cases.
• Partner with validation and QA teams to rigorously test model releases in simulated scenarios, detecting regressions and 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 hardware, electrical, and firmware teams on custom carrier boards, sensor interfaces, and edge GPUs.
• Benchmark and deploy models using 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 from multi-petabyte multi-sensor datasets.
• Extract structural corner cases from real-world driving logs to continuously feed and improve end-to-end behavior models.
• Collaborate with QA, testing, and simulation teams to transform real-world anomalies into concrete data blocks for simulation.
• Implement programmatic data curation, active learning strategies, and statistical quality metrics to optimize training signal-to-noise ratio.

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.

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 at the forefront of building Physical AI—real-world robots—for critical industries such as food, mining, and transport. Our mission is to enhance the intelligence, efficiency, and capabilities of the physical world. We achieve this by creating systems that accurately understand, predict, and control real-world operations, transforming complex physical tasks into reliable, scalable, and productive processes. As a team of roboticists, engineers, operators, and builders, we are dedicated to solving hard problems with real-world impact, pushing the boundaries of what's known, and building what doesn't yet exist. We prioritize a collaborative environment where continuous innovation and growth are fostered. Our operations are based in our San Francisco office, where all office-based teams work five days a week.

How to Apply

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