Amazon RIVR 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 Amazon RIVR is building the next generation of safe reliable autonomous robots for last-mile delivery
Job Description
Our global fleet of autonomous robots operates in the real world generating vast amounts of multi-modal sensor data. While our VLA team focuses on building large-scale models to consume this data much of it remains unlabeled and unstructured. We are seeking an expert in self-supervised and representation learning to unlock the full potential of this massive data pool.
In this role you will be responsible for designing and building the core data engine that transforms raw real-world sensor data into high-signal structured datasets suitable for training neural networks. You will pioneer methods to automatically curate filter and pseudo-label this data creating powerful representations that serve as the foundation for all downstream tasks including navigation imitation learning and decision-making.
You will work directly with the VLA and Reinforcement Learning teams to define data strategies and interfaces ensuring the data you produce directly accelerates their model development. If you are passionate about solving the data bottleneck in robotics and want to build the systems that learn meaningful patterns from the physical world we invite you to join us.
What youll be doing
- Design build and maintain scalable data pipelines to process filter and transform terabytes of raw multi-modal sensor data (e.g. video LiDAR IMU odometry) from our robotic fleet.
- Develop and implement state-of-the-art self-supervised and representation learning algorithms to automatically extract features discover patterns and generate pseudo-labels from our unlabeled data.
- Collaborate closely with the VLA Foundation Model and RL teams to define data requirements APIs and strategies for leveraging curated datasets and learned representations.
- Architect and implement robust evaluation strategies benchmarks and datasets to rigorously track the performance and quality of both the data pipeline and the downstream models that consume it.
- Own the data integration workflow creating efficient data loaders and access patterns to make high-signal data readily available for model training and experimentation.
- Research and prototype novel techniques in data curation active learning and anomaly detection to continuously improve the quality and efficiency of our data engine.
What you must have
- Masters degree or higher in a relevant field such as Computer Science Machine Learning or Robotics.
- A minimum of three years of industry or research experience with PhD experience applicable.
- Deep expertise in self-supervised learning (SSL) and representation learning particularly with multi-modal sensor data (e.g. contrastive learning masked autoencoders world models).
- Proven experience in building and managing large-scale data processing pipelines for machine learning (e.g. using Spark Kubeflow or similar cloud-native tools).
- Strong understanding of robotic sensor data (e.g. camera LiDAR IMU odometry) and their characteristics.
- Strong programming skills in Python and deep experience with PyTorch including creating custom and efficient DataLoaders.
- Experience with MLOps best practices and data versioning tools (e.g. DVC Pachyderm)
Get some bonus points
- PhD degree in Robotics Engineering Computer Science Machine Learning or a similar discipline or an equivalent amount of research experience.
- Publications at top-tier ML or robotics conferences (e.g. NeurIPS ICML CVPR CoRL ICLR).
- Experience with generative models (e.g. GANs Diffusion Models) for data augmentation or simulation.
Amazon RIVR is committed to building a diverse and inclusive team that values every perspective. If youre 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
Senior IC
Amazon RIVR 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 ...
Amazon RIVR 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 Amazon RIVR is building the next generation of safe reliable autonomous robots for last-mile delivery
Job Description
Our global fleet of autonomous robots operates in the real world generating vast amounts of multi-modal sensor data. While our VLA team focuses on building large-scale models to consume this data much of it remains unlabeled and unstructured. We are seeking an expert in self-supervised and representation learning to unlock the full potential of this massive data pool.
In this role you will be responsible for designing and building the core data engine that transforms raw real-world sensor data into high-signal structured datasets suitable for training neural networks. You will pioneer methods to automatically curate filter and pseudo-label this data creating powerful representations that serve as the foundation for all downstream tasks including navigation imitation learning and decision-making.
You will work directly with the VLA and Reinforcement Learning teams to define data strategies and interfaces ensuring the data you produce directly accelerates their model development. If you are passionate about solving the data bottleneck in robotics and want to build the systems that learn meaningful patterns from the physical world we invite you to join us.
What youll be doing
- Design build and maintain scalable data pipelines to process filter and transform terabytes of raw multi-modal sensor data (e.g. video LiDAR IMU odometry) from our robotic fleet.
- Develop and implement state-of-the-art self-supervised and representation learning algorithms to automatically extract features discover patterns and generate pseudo-labels from our unlabeled data.
- Collaborate closely with the VLA Foundation Model and RL teams to define data requirements APIs and strategies for leveraging curated datasets and learned representations.
- Architect and implement robust evaluation strategies benchmarks and datasets to rigorously track the performance and quality of both the data pipeline and the downstream models that consume it.
- Own the data integration workflow creating efficient data loaders and access patterns to make high-signal data readily available for model training and experimentation.
- Research and prototype novel techniques in data curation active learning and anomaly detection to continuously improve the quality and efficiency of our data engine.
What you must have
- Masters degree or higher in a relevant field such as Computer Science Machine Learning or Robotics.
- A minimum of three years of industry or research experience with PhD experience applicable.
- Deep expertise in self-supervised learning (SSL) and representation learning particularly with multi-modal sensor data (e.g. contrastive learning masked autoencoders world models).
- Proven experience in building and managing large-scale data processing pipelines for machine learning (e.g. using Spark Kubeflow or similar cloud-native tools).
- Strong understanding of robotic sensor data (e.g. camera LiDAR IMU odometry) and their characteristics.
- Strong programming skills in Python and deep experience with PyTorch including creating custom and efficient DataLoaders.
- Experience with MLOps best practices and data versioning tools (e.g. DVC Pachyderm)
Get some bonus points
- PhD degree in Robotics Engineering Computer Science Machine Learning or a similar discipline or an equivalent amount of research experience.
- Publications at top-tier ML or robotics conferences (e.g. NeurIPS ICML CVPR CoRL ICLR).
- Experience with generative models (e.g. GANs Diffusion Models) for data augmentation or simulation.
Amazon RIVR is committed to building a diverse and inclusive team that values every perspective. If youre 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
Senior IC
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Not Disclosed
Posted on:
30+ days ago
Vacancies:
1 Vacancy
