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About the Role

Atoms is seeking a visionary Machine Learning Engineer to join our founding team. This role will bridge the gap between AI research and real-world physical actuation for autonomous transport platforms. We are hiring across three specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.

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-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.

Requirements

• 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.
• Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization is highly preferred.
• 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++.
• Familiarity with low-level camera/sensor interfaces and robotics hardware is a significant plus.
• 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.
• Exceptional ability to navigate, structure, and derive signal from highly ambiguous, messy, or undefined real-world data distributions.

Skills

• PyTorch
• JAX
• Python
• C++
• TensorRT
• CUDA
• Transformers
• RL
• Distillation
• Multimodal systems
• VLA models
• Diffusion models
• Sensor Fusion
• LiDAR
• Radar
• Multi-task learning
• Birds-Eye-View (BEV)
• Representation learning
• Data tokenization
• Model optimization
• Model distillation
• Model deployment
• Edge deployment
• Embedded GPU
• Profiling tools
• Runtime compilation
• Camera interfaces
• Sensor interfaces
• Robotics hardware
• Data curation pipelines
• Active learning workflows
• Data mining architectures
• Robotics data structures
• Spatial frameworks

Location

• San Francisco

Work Type

• Onsite

Experience Level

• 4+ years of non-internship professional MLE experience

Salary/Compensations

• $208,000 - $263,500

Benefits

• Equity awards
• Annual performance-based bonus
• 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 the Company

• Atoms is building the machines that power the next era of progress.
• 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.
• We are roboticists, engineers, operators, and builders.
• We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life.
• At Atoms, you’ll work on one of the defining challenges of our time - bringing automation into the physical world to drive real, lasting impact.
• We exist to uncover valuable unknown truths and turn them into progress, which means constantly pushing beyond what’s known and building what doesn’t yet exist.
• The work is ambitious and often challenging, but it’s grounded in a shared sense of purpose and a team committed to seeing it through together.
• Our work only matters if it serves others, and we know that meaningful progress depends on the trust of the people we serve and the strength of our team—so we invest in both, creating an environment where you can do your best work and grow.
• Atoms is a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate.
• We do our best work together.