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SeniorFull-timePythonAWSMachine LearningCI/CDGoAIML
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for a Senior Data Scientist to help build, ship, and scale production AI systems from the ground up. This role focuses on engineering-forward ML/AI systems, requiring candidates who can move from prototype to production and own critical parts of the ML/AI systems lifecycle.
Key Responsibilities
- →Architect, build, and deploy production ML/AI systems that power customer-facing product capabilities
- →Design and operate scalable inference services, APIs, and backend components for model-driven applications
- →Build and improve data, feature, deployment, and orchestration pipelines on AWS
- →Productionize AI workflows with strong MLOps practices, including CI/CD, versioning, testing, monitoring, and operational reliability
- →Define and implement evaluation frameworks for model quality, system reliability, latency, and cost
- →Partner with product, research, and engineering teams to turn prototypes into robust, scalable services
Requirements
- ✓6+ years of experience in software engineering, machine learning engineering, or a closely related role with production ownership
- ✓Demonstrated experience taking ML/AI systems from prototype to production in live environments
- ✓Strong experience with deployment pipelines, CI/CD, orchestration, and operating production services on AWS
- ✓Experience building and operating APIs/services (Python preferred), working with containers, and debugging reliability/performance issues
- ✓Working knowledge of modern AI application patterns and the engineering constraints involved in running them in production
- ✓Strong communication skills and the ability to work through ambiguity across engineering, product, and research teams
Benefits
- ★Competitive compensation and participation in Instructure’s equity program
- ★Flexible schedules and a remote-friendly culture
- ★Annual "Dim the Lights" company-wide shutdown
- ★Comprehensive wellness programs and mental health support
- ★Annual learning and development stipends
- ★Technology and tools provided to do your best work
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