Entry-Level Robot Learning Engineer

Clera

Munich, Germany

On-site

Who this role is best for

Geared toward early-career candidates with a technical background in robotics or AI, comfortable with deploying machine learning models in industrial settings.

Best fit for

Early-career candidates with a robotics or AI degree and hands-on experience in simulation or sensor data processing
— “Experience with simulation or sim-to-real transfer, such as Isaac Sim, PyBullet, or MuJoCo, is helpful.”
Individuals with a strong foundation in deep learning and computer vision, particularly with sensor data
— “Apply computer vision and deep learning to camera, force and torque, and depth sensor data.”
Candidates who have worked on or contributed to open-source robotics or machine learning projects
— “Research publications or open-source contributions in robotics or machine learning are also valued.”

Things to consider

Must be eligible to work in Germany and commit to on-site work in Munich five days per week
— “Must be eligible to work in Germany and able to work on-site in Munich five days per week.”
No visa sponsorship means non-EU candidates may face barriers to applying
— “Visa sponsorship is not available.”

How to stand out

Highlight experience with ROS 2 and specific AI model integration in your resume and interview stories
— “Integrate AI models into a ROS 2 robotics stack.”
Showcase your ability to transition research into practical applications with concrete examples
— “Help bridge robotics research with deployment on industrial hardware.”
Emphasize your work with reinforcement learning or imitation learning in robotics contexts
— “Experiment with reinforcement learning and imitation learning for robot control.”
Demonstrate familiarity with 3D perception, point clouds, or depth estimation in your application materials
— “Familiarity with 3D perception, point clouds, or depth estimation is helpful.”

Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Individual

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

What success looks like

Develop and evaluate machine learning models
Apply computer vision and deep learning
Experiment with reinforcement learning and imitation learning
Integrate AI models into a ROS 2 robotics stack
Run experiments, analyze results, and iterate on approaches

Typical background

0 to 3 years of experienceBachelor's or master's degree in robotics, computer science, AI/ML, or a related field

Skills & requirements

Required

PythonDeep LearningComputer VisionReinforcement LearningImitation LearningROS 2

Preferred

Simulation3D PerceptionPoint CloudsDepth Estimation

Stack & domain

PythonPyTorchTensorFlowROS 2

About the role

Original posting from Clera via Ashby

ABOUT THE ROLE
Join an industrial robotics team applying machine learning to challenging manufacturing tasks. You will develop and evaluate robot learning approaches, then help bring them from research into real-world factory deployment.
WHAT YOU'LL DO
Research and evaluate machine learning models for robot perception and task understanding.
Apply computer vision and deep learning to camera, force and torque, and depth sensor data.
Experiment with reinforcement learning and imitation learning for robot control.
Integrate AI models into a ROS 2 robotics stack.
Run experiments, analyze results, and iterate on approaches.
Help bridge robotics research with deployment on industrial hardware.
WHAT WE'RE LOOKING FOR
Approximately 0 to 3 years of experience, with a bachelor's or master's degree in robotics, computer science, AI/ML, or a related field, or equivalent practical experience.
Proficiency in Python and experience with a deep learning framework such as PyTorch or TensorFlow.
Exposure to computer vision or robot learning.
Familiarity with ROS or ROS 2 is helpful.
Experience with simulation or sim-to-real transfer, such as Isaac Sim, PyBullet, or MuJoCo, is helpful.
Familiarity with 3D perception, point clouds, or depth estimation is helpful. Research publications or open-source contributions in robotics or machine learning are also valued.
Fluent English is required; German is a plus.
Must be eligible to work in Germany and able to work on-site in Munich five days per week.
COMPENSATION & BENEFITS
Equity participation is available. Visa sponsorship is not available.
LOCATION
On-site in Munich, Bavaria, Germany, five days per week.

Source: Clera careers (Ashby)