Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

You will..

- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.

- Collaborate with data scientists and engineers to understand model requirements and optimize pipeline processes.

- Automate the training, testing and deployment processes for machine learning models.

- Continuously monitor and maintain model pipelines, ensuring optimal performance, accuracy and reliability.

- Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.

- Ensure compliance with security and data privacy standards in all MLOps activities.

Qualifications:

- 3-5 years of experience in MLOps, DevOps or a related field.

- Bachelor’s degree in Computer Science, Data Science or a related field.

- Strong understanding of machine learning principles and model lifecycle management.

- Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow or PyTorch.

- Experience with cloud platforms like AWS, Azure, or Google Cloud and their respective machine learning services.

- Familiarity with containerization and orchestration tools such as Kubernetes and Docker.

- Knowledge of CI/CD pipelines, automation tools and version control systems like Git.

- Strong problem-solving skills and ability to troubleshoot complex issues.

- Experience with monitoring tools and practices for model performance in production.

- Ability to work collaboratively in cross-functional teams.

Bonus/nice to have:

- Experience with infrastructure-as-code (IaC) tools such as Terraform or Crossplane.

- Knowledge of big data technologies like Apache Spark or Hadoop.

- Familiarity with data engineering practices and tools.

- Experience with A/B testing and model validation in production environments.

- Relevant MLOps certifications (e.g., AWS Certified Machine Learning – Specialty, DataRobot MLOps Certification) are a plus.