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, working closely with cross-functional teams in a remote environment.
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 machine learning models on the edge.
- Experience building end-to-end machine learning applications from scratch.
- Expertise in optimizing distributed model training with GPUs.
- Ability to design and implement scalable machine learning infrastructure using Ray to support model training, deployment, and inference at scale.
- Ability to leverage Kubernetes for orchestration of containerized applications, ensuring seamless deployment, scaling, and management of ML models and associated services.
- Experience developing and maintaining CI/CD pipelines for automated testing, deployment, and management of ML applications and infrastructure.
- Ability to implement robust monitoring, logging, and alerting systems to ensure high availability, performance, and security of the ML platform.
- Ability to collaborate with data scientists and ML engineers to optimize data pipelines and model performance.
- Ability to stay abreast of the latest advancements in machine learning technologies and infrastructure, and advocate for the adoption of best practices and new technologies within the team.
- Provide DevOps SRE support for the ML platform, including incident response, performance tuning, and disaster recovery planning.
Benefits & Perks
Competitive total compensation package including base salary, bonus, and restricted stock unit awards (RSUs)
Remote and flexible working options
Health benefits
Ready to Apply?
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