Software Engineer 5 – Model Development and Management, AI Platform
New
J
JobgetherAI/ML platform
Work remotely from within the United StatesFull-TimeSenior
SalaryAnnual compensation range of $466,000–$750,000. Annual compensation is provided through salary and stock options, with employees able to choose their preferred allocation each year. The compensation structure does not include bonuses.
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Job Details
- Experience
- At least five years of full-time software engineering or machine learning experience
- Required Skills
- PythonJavaKubeflowC++AirflowScalaDistributed Systems
Requirements
- Have at least five years of full-time software engineering or machine learning experience.
- Have a bachelor's and master's degree or a PhD.
- Demonstrate software design and development skills in Python.
- Be proficient in at least one additional language such as Scala, Java, or C++.
- Have experience designing robust, maintainable applications and working with distributed systems.
- Have experience developing platform solutions, SDKs, developer frameworks, or internal tools for technical users.
- Understand the end-to-end machine learning lifecycle, including data and feature pipelines, model training, experiment tracking, evaluation, and production deployment.
- Be familiar with ML workflow orchestration, experiment tracking, or feature and data infrastructure; tools may include Metaflow, Airflow, Kubeflow, or MLflow.
- Understand core machine learning concepts and their practical application in large-scale systems.
- Communicate and collaborate effectively across multidisciplinary teams.
Responsibilities
- Design, build, and maintain scalable platform solutions, including SDKs, developer frameworks, internal tools, and user interfaces.
- Develop tools and services for model creation, evaluation, experimentation, and deployment.
- Support workflows for data and feature management, model training, experiment tracking, and production integration.
- Apply software engineering principles to build maintainable, efficient, resilient distributed applications.
- Partner with applied researchers, data scientists, ML engineers, and other engineering teams to gather requirements and deliver solutions.
- Engage with the machine learning community, collect feedback, and translate user needs into platform improvements.
- Contribute to workflow orchestration, experiment tracking, and feature or data infrastructure.
- Explore emerging machine learning technologies and contribute to open-source projects where appropriate.
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