mimic is an early-stage deep tech robotics & AI start-up based in Zurich and supported by leading VCs. We give industry workers a helping hand for tedious manual labour tasks and mitigate labour shortages with a versatile automation platform. Our automation solutions, driven by dexterous robotic hands and cutting-edge AI trained on human observations, bring a new level of AI embodiment to the real world.

The Role

We are looking for an experienced MLOps / Infrastructure Engineer to develop and maintain our machine learning and cloud compute stack. Your mission will be to empower the AI team by automating ML pipelines and managing our training infrastructure, allowing them to train models from single to multi-node setups without worrying about dependencies or cloud provider complexities. You will be the foundation that enables our AI team to work effectively and accelerate our progress.

What you’ll do

• Develop, automate, and maintain our end-to-end ML pipelines.
• Manage containerization and CI/CD for our model training and serving stack.
• Design and support the infrastructure for multi-node model training, including model sharding and other optimization techniques.
• Manage our cloud provider relationships and infrastructure to ensure efficiency and scalability.
• Monitor model training runs, identify bottlenecks, and implement performance improvements.
• Collaborate closely with the AI team to understand their needs and streamline their workflows.

Who you are

• 3+ years of experience in MLOps for deep learning training.
• You have deep expertise in developing and maintaining machine learning and cloud compute infrastructure stacks.
• You are proficient with containerization technologies (e.g., Docker, Kubernetes) and CI/CD pipelines for ML.
• You have hands-on experience with multi-node training and cloud providers (e.g., AWS, GCP, Azure).
• You are a clear communicator, able to collaborate effectively in a team environment.
• You are a fluent English speaker.

Nice to have

• Experience in a fast-paced robotics, AI, or deep-tech startup environment.
• Working knowledge of recent robot learning developments (e.g. imitation learning, VLAs), as well as reinforcement learning and simulation frameworks.