(Senior) AI Engineer - Reinforcement Learning Manipulation

Rivr

Zürich, Switzerland

On-site

Who this role is best for

Best suited to candidates with deep reinforcement learning and robotics manipulation expertise working in real-world autonomous delivery systems.

Best fit for

Candidates with strong reinforcement learning and real-world robotics deployment experience
— “Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation”
Individuals with a track record in solving complex manipulation challenges with sensor data
— “Design, test, and refine algorithms to solve complex real-world manipulation challenges”
Professionals who have published in top-tier robotics conferences and have hands-on hardware deployment experience
— “Publications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL”

Things to consider

In-person presence is required despite the flexibility of full-time employment
— “We believe the best work is done when collaborating and therefore require in-person presence in our office locations”

How to stand out

Highlight experience with sim-to-real transfer and real-world deployment of RL algorithms
— “innovate methods that leverage both simulated and real-world data”
Emphasize work with tactile sensing, multi-fingered hands, or bimanual manipulation in your resume
— “Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation”
Showcase your ability to write production-level C++ code and prototype in Python
— “Ability to write production-level code in modern C++”
Demonstrate expertise in policy optimization and exploration-exploitation strategies in RL
— “reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms”

Pace · SteadyCollaboration · MediumAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

successful deployment of reinforcement learning algorithms
improved manipulation performance
publications in top-tier conferences

Typical background

roboticsmachine learningcomputer science

Skills & requirements

Required

Reinforcement LearningRobotic ManipulationDeep LearningNeural NetworksAutonomy

Preferred

Tactile SensingMulti-fingered Robotic HandsBimanual Manipulation

Stack & domain

Robotic ManipulationDynamicsGrasp SynthesisTrajectory OptimizationSupervised LearningSelf-supervised LearningReinforcement LearningMarkov Decision Processes (mdps)Neural Network ArchitecturesPolicy Optimization AlgorithmsModel-based Vs. Model-free RLExploration-exploitation StrategiesValue Function MethodsTransfer LearningDomain AdaptationSim-to-real TransferRoboticsAutonomyManipulation

About the role

Original posting from Rivr via Lever

RIVR, part of Amazon is a robotics company pioneering Physical AI through real-world doorstep delivery. Founded in 2024 as an ETH Zurich spin-off, RIVR developed wheeled-legged robots designed to operate in complex, unstructured environments such as stairs, gates, doors, and uneven urban terrain. We believe that achieving general physical intelligence requires solving real customer problems in the real world, where robots can learn from rich operational data at scale.
Following our acquisition by Amazon in March 2026, we are continuing this mission with greater reach and speed. By combining custom robot hardware, onboard autonomy, and cloud-based coordination, RIVR, part of Amazon is building the next generation of safe, reliable autonomous robots for last-mile delivery
What you’ll be doing:
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Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands.
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Design, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches.
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Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data.
What you must have:
Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization.
Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
A minimum of five years of industry or research experience, with PhD experience applicable.
Strong deep learning fundamentals, including supervised and self-supervised learning techniques, and reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms, model-based vs. model-free RL, exploration-exploitation strategies, value function methods, transfer learning, domain adaptation, sim-to-real transfer, etc.
Strong background in robotics including autonomy and/or 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.
Get some bonus points:
PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
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Publications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.
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Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.
RIVR, part of Amazon is committed to building a diverse and inclusive team that values every perspective. If you’re passionate about driving innovation in robotics and creating meaningful impact, we encourage you to apply and bring your unique self to our team.
We believe the best work is done when collaborating and therefore require in-person presence in our office locations.

Source: Rivr careers (Lever)