Start Date: ASAP

About Us

Mundane is a venture-backed seed-stage robot learning startup founded by a team of Stanford researchers and builders. We’re deploying a massive fleet of humanoid robots to perform mundane tasks in commercial environments, collecting data to build the next generation of embodied intelligence.

We are a fast-paced, execution-driven team of engineers, roboticists, and builders. Our robots operate in real customer environments — and improve through real-world experience.

About the Role

You will develop and ship learning-based manipulation policies that run on real robots.

Our robots already collect real-world data and execute manipulation tasks. Your role is to turn that data into policies that improve reliability, generalize across tasks, and hold up under real-world distribution shift.

This role is deeply hands-on and execution-focused. You will implement models, run controlled experiments, and validate improvements directly on physical robots. Success is measured by real-world performance, not benchmark metrics.

At Mundane, your models will not live in simulation or papers — they will deploy to humanoid robots operating in customer environments.

What You’ll Own

• Development and improvement of real-world manipulation policies
• Policy architecture and training recipes for real-world manipulation
• Robustness improvements (recovery behaviors, partial observability, drift, edge cases)
• Experiment discipline and clear ablation methodology
• Scaling from single-task policies to multitask robot capabilities
• Packaging models for deployment on real robots

Responsibilities

• Extend and improve our policy learning stack (imitation learning / sequence-based policies) for real-world manipulation tasks
• Design and run disciplined experiments to improve policy performance, including clear ablations and controlled comparisons
• Develop multitask policies with effective task conditioning and thoughtful data mixture strategies
• Improve robustness through techniques such as data augmentation, recovery behaviors, and training under partial observability
• Design and run systematic stress tests to evaluate distribution shift, drift, and edge-case failures
• Work closely with infrastructure engineers to scale training pipelines and experiment workflows
• Collaborate with reliability engineers to define evaluation gates and deployment criteria
• Package trained models for deployment, addressing latency, stability, and safety constraints
• Investigate real-world failures and iterate rapidly to improve policy robustness

Qualifications

• Strong PyTorch and ML engineering skills with the ability to implement and ship reliable training pipelines
• Practical experience with imitation learning or behavior cloning
• Experience training sequence-based models such as transformers, diffusion policies, or related architectures
• Comfort running real-world experiments and debugging issues across data, training, and deployment
• Strong experimental rigor, including designing ablations, maintaining reproducibility, and avoiding “demo-only” improvements

Nice to Have

• Experience with robotic manipulation systems and real-world robot experimentation
• Familiarity with common failure modes in manipulation tasks
• Experience scaling training across large datasets or multi-GPU environments
• Background in embodied AI or robot learning systems

What You’ll Get

• Direct ownership over the policies that control robots operating in real environments
• Early equity with meaningful upside in a venture-backed robotics company
• The opportunity to see your research deployed quickly on real humanoid robots
• Close collaboration with hardware, infrastructure, and deployment teams
• A front-row seat in scaling a technically ambitious robotics company from seed stage

Perks: Competitive salary + equity, flexible PTO, legendary merch, coffee, robots, sauna & cold plunge (pending)