Imitation Learning Engineer, RIVR CH

AI in this role

dall-estable-diffusion

RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one.

Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies. By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills. We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics.

Key job responsibilities
Develop imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data.
Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks.
Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms.
Streamline the workflow to efficiently expand the imitation learning dataset with new tasks.
Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data.
Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints.
Build, lead and mentor an exceptional team of software engineers.
Provide expert guidance to product managers and executives for strategic decision-making.
Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.

Basic qualifications

- Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- 5 years of industry or research experience, with PhD experience applicable.
- Strong deep learning fundamentals including supervised learning, self-supervised learning, neural network architectures, policy optimization algorithms, imitation learning, and generative AI techniques, including Diffusion Models such as DALL-E 2 and Stable Diffusion, as well as Diffusion Policy.
- Background in robotics including autonomy and manipulation.
- Experience with deploying artificial neural networks on hardware platforms.
- Ability to write production-level code in modern C++.
- Ability to prototype algorithms and train deep neural networks in Python.

Preferred qualifications

- PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
- Publications at top-tier conferences.
- Experience in managing a software team.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

How we rate this

Imitation Learning Engineer, RIVR CH at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Imitation Learning Engineer, RIVR CH is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in CH, Zurich.
The job was posted on October 6, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Dall EStable Diffusion

Questions you could be asked

What's a project where you used Dall E hands-on?
Walk me through how you've used Stable Diffusion in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Dall E and Stable Diffusion. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

Want an expert to read your CV for this job?

Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.

Get a free CV review