Machine Learning Engineer (all genders)
Tasks
- Automate experimentation to production workflows
- Build high throughput low latency batch and real time feature pipelines
- Collaborate with applied scientists to improve model performance
- Design end to end data engineering and MLOps solutions
- Implement CI CD pipelines and automated monitoring
- Operationalize model serving and maintenance for real time inference systems
- Optimize data pipeline runtime and data quality
- Set up continuous monitoring and automated alerting for distributed systems
- Translate engineering concepts into actionable roadmaps
Perks/Benefits
- Discounts on products
- Employee shares
- Health & wellbeing benefits
- Hybrid work
- Mental health support
- Paid volunteering days
- Relocation assistance
- Remote work
- Training and development
- Work from abroad
Skills/Tech-stack
AWS | Agile | Airflow | Amazon SageMaker | Apache Flink | Apache Spark | Automated alerting | CI/CD | Continuous Monitoring | Data Engineering | Data Pipelines | Data version control | Databricks | Databricks Workflows | Feature Engineering | Hopsworks | Java | Kubernetes | MLOps | MLaaS | Machine Learning | Metrics monitoring | Model Serving | PyTorch | Python | Streaming Analytics | TensorFlow | Triton | Version control | Workflow Orchestration
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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