Machine Learning Engineer – Physical AI / MLOps

Oversonic Robotics is a leading company in the field of cognitive humanoid robotics, founded in 2020 and focused on developing cognitive humanoid robotics for progress and well-being. Our mission is to make the benefits of the future accessible by creating innovative solutions for industry and healthcare. We aim to support people in repetitive, dangerous, and strenuous jobs with technological solutions that integrate with daily life and respect the environment.

We are looking for a Machine Learning Engineer (Mid/Senior) to work on ML infrastructure and production pipelines for Physical AI and robotics systems.

The role is focused on:

• ML pipeline orchestration
• training/inference workflows
• model deployment
• data processing for sensor and telemetry streams
• MLOps and infrastructure automation

The position is engineering-oriented and production-focused.

• Build and maintain ML pipelines for robotics and Physical AI applications
• Manage training, evaluation, deployment, and monitoring workflows
• Develop data and feature pipelines for sensor and time-series data
• Work on edge/cloud ML integration
• Improve reliability and observability of ML systems
• Containerize and deploy ML services
• Collaborate with robotics, cloud, and data teams
• Support predictive maintenance and anomaly detection pipelines

• Strong Python skills
• Experience deploying ML systems in production
• Experience with Docker and Kubernetes
• Experience with ML pipelines and MLOps workflows
• Familiarity with CI/CD and infrastructure automation
• Linux and Git
• Experience handling real-world noisy datasets

• PostgreSQL / TimescaleDB
• Kafka or streaming systems
• Robotics or IoT systems
• Time-series data
• Edge AI deployments
• GPU workloads
• Monitoring/observability tooling

• Python
• Docker
• Kubernetes
• PostgreSQL / TimescaleDB
• ML pipelines
• Time-series analytics

We are open to:

• mid-level engineers with solid ML engineering foundations
• senior engineers capable of driving production ML infrastructure and architecture