At Waymo, we’re not just building models; we’re creating the future. Our team thrives on innovation, blending creativity with cutting-edge technology to solve real-world problems. We empower industries, enhance experiences, and make data-driven decisions more impactful.
Whether it’s training models that understand human emotions, optimizing supply chains with precision, or building scalable AI infrastructure, we are redefining what’s possible. And now, we’re looking for you to help us push the boundaries.
What You’ll Do:
As a Machine Learning Engineer specializing in ML Ops, you will:
- Build & Scale: Design, develop, and deploy ML pipelines to production, ensuring seamless scalability and efficiency.
- Optimize: Collaborate with data scientists to fine-tune models and maximize performance in real-world environments.
- Innovate: Experiment with new algorithms, tools, and frameworks, keeping us ahead of the ML curve.
- Automate: Develop robust CI/CD systems for ML workflows, ensuring automated testing, monitoring, and retraining.
- Collaborate: Work closely with cross-functional teams, including engineers, product managers, and data analysts, to integrate machine learning solutions into products.
- Monitor & Maintain: Ensure the stability and performance of deployed models with real-time monitoring and troubleshooting.
What Makes You a Fit:
We value a passion for innovation over rigid checklists, but here’s what will make you stand out:
Technical Expertise:
- Hands-on experience with Python, TensorFlow, PyTorch, and other ML libraries.
- Proficiency in cloud platforms like AWS, GCP, or Azure.
- Knowledge of containerization and orchestration tools like Docker and Kubernetes.
Problem-Solving Skills:
- Strong analytical mindset with experience in optimizing ML workflows.
- Ability to troubleshoot issues in production environments.
Bonus Points:
- Experience with large-scale data systems (e.g., Spark, Hadoop).
- Familiarity with ML monitoring tools (e.g., MLflow, Kubeflow, SageMaker).
- Previous work on explainable AI or model interpretability.
