5 days ago
Base Salary
$273k - $321k/yr
Responsibilities
- Define and build technical architecture for action models and learned decision-making systems.
- Develop models that translate learned physical-world representations into decisions, plans, and actions.
- Research reasoning, planning, control, action generation, imitation learning, reinforcement learning, and behavior learning approaches.
- Develop learned policies and action heads for real-world robotics and autonomous systems.
- Connect perception and world models to downstream decision-making and control across temporal horizons.
- Train and evaluate models using real-world, simulated, and synthetic data.
- Develop learning methods using demonstrations, human behavior, robot experience, and other supervision sources.
- Make architectural decisions across data, model design, training, evaluation, inference, and deployment.
- Establish evaluation methods for reasoning, planning, action quality, robustness, and generalization.
- Translate embodied-intelligence research into reliable systems operating on real machines.
- Provide technical leadership through research direction, architecture reviews, mentorship, experimentation, and hands-on engineering.
- Partner with perception, world-model, robotics, autonomy, simulation, and ML infrastructure teams and help assess engineering and research talent.
Requirements
- Deep expertise in machine learning applied to decision-making, robotics, autonomous systems, or embodied intelligence.
- Strong understanding of modern deep learning architectures for sequential decision-making and physical systems.
- Experience in one or more of reinforcement learning, imitation learning, behavior learning, planning, control, robot learning, or embodied AI.
- Experience connecting learned representations or perception to downstream actions.
- Strong understanding of sequential and temporal modeling and how actions influence future states.
- Experience training and evaluating models with large-scale real-world, simulated, or synthetic datasets.
- Understanding of the full ML lifecycle, including data strategy, model architecture, training, evaluation, optimization, and inference.
- Experience translating research ideas into functioning machine learning systems.
- Strong software engineering fundamentals and hands-on ability in Python and/or C++.
- Ability to work in ambiguous research spaces and make consequential technical decisions that influence research or engineering direction.
- Ability to communicate complex research and technical ideas and collaborate across research, engineering, and robotics disciplines.
Benefits
- Medical, dental, vision, disability, and life insurance.
- Flexible Spending Account and Health Savings Account options.
- 401(k), equity eligibility, sick time, unlimited flexible time off, paid holidays, and paid parental leave.
- Pre-tax commuter benefit plan and team lunch in the SoMa office every Tuesday and Thursday.
- Based in the San Francisco office with onsite work five days per week for office-based teams.
- Full-time exempt USA employee benefits; benefits are subject to change at the company's discretion.
Categories
ML EngineeringRobotics
About Atoms
Atoms develops robotics, software, and infrastructure to automate real-world industrial operations, with offerings focused on food production, mining, and robotic transport platforms. It sells integrated automation systems and platforms—Atoms Food, Atoms Mining, and a transport wheelbase for robots—to industrial customers seeking greater productivity and scalability. The privately held company is headquartered in Los Angeles.