Senior Machine Learning Infrastructure Engineer

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

As a Senior Machine Learning Infrastructure Engineer, you will be responsible for building and maintaining the next generation of Hyperscience’s ML Platform and Infrastructure. You will lead initiatives geared towards making the ML Engineers and Applied Scientists at Hyperscience more productive. You will make improvements to and extend the underlying infrastructure that powers the ML teams, thus simplifying the development and deployment cycles of ML models. You will help establish best practices for the ML pipeline and partner with other infrastructure ops teams to help champion them across the company.

Responsibilities

• Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning at Hyperscience
• Collaborate with teams to drive the ML technical roadmap
• Collaborate with Machine Learning Engineers and Product Managers to develop tools to support experimentation, training and production operations
• Build and maintain data pipelines using tools like Hadoop, Python, Airflow, and Kafka
• Offer support and troubleshooting assistance for the ML pipeline, while continuously improving stability along the way
• Build and maintain systems employing an Infrastructure-as-Code approach
• Own the AWS stack which comprises all ML resources
• Establish standards and practices around MLOps, including governance, compliance, and data security
• Collaborate on managing ML infrastructure costs

Qualifications

• 3+ years of experience with ML infrastructure and ML DevOps
• 5+ years of overall engineering experience in distributed systems and data infrastructure
• 3+ years experience coding in Python (preferred) or other languages like Java, C#, Golang etc. 
• Experience working with ML engineers to build tooling and automation to support the entire ML engineering lifecycle, from experimentation to production operations
• Experience with Kubernetes and ML CI/CD workflows
• 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.)
• Excellent verbal and written communication skills.

Nice to haves

• Experience with Infrastructure-as-Code tools and frameworks
• #Li-Remote