Job Description
The role involves developing and maintaining scalable machine learning infrastructure and applications to enhance the safety, efficiency, and sustainability of physical operations across various industries, collaborating with cross-functional teams, and deploying ML models at scale.
Key Responsibilities
- Design and implement scalable machine learning infrastructure using Ray for model training, deployment, and inference.
- Leverage Kubernetes for orchestration of containerized ML applications and services.
- Develop and maintain CI/CD pipelines for automated testing, deployment, and management of ML applications.
- Implement monitoring, logging, and alerting systems to ensure high availability, performance, and security of the ML platform.
- Collaborate with data scientists and ML engineers to optimize data pipelines and model performance.
- Provide DevOps SRE support for the ML platform, including incident response, performance tuning, and disaster recovery planning.
Requirements
- Minimum requirements for the role BS or MS in Computer Science or other relevant field.
- 6 years of experience as a Machine Learning Engineer, Applied Scientist, or similar role.
- Strong proficiency in one or more common programming languages such as C++, Golang, Java, Python, or Scala.
- Proficiency with common machine learning tools such as Spark, TensorFlow, or PyTorch.
- Experience deploying and iteratively refining models using customer feedback loops.
- Comfortable with full-stack backend development code to build a strong understanding of underlying data structures and other dependencies.
- Experience building, deploying, and optimizing ML models on the edge.
- Experience building end-to-end ML applications from scratch.
- Expertise in optimizing distributed model training with GPUs.
Benefits & Perks
Competitive total compensation package including base salary, bonus, and RSUs
Remote and flexible working options
Health benefits
Ready to Apply?
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