robot learning research engineer (manipulation policies)

Start Date: ASAP

Are you the right candidate for this opportunity Make sure to read the full description below.

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 xywuqvp 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)