Tech Lead Manager, Model Integration
New
Opportunity to work remotely within the United StatesFull-TimeManager
Salary$218,800 - $335,300 USD, depending on experience, location, skills, and qualifications.
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSoftware DevelopmentMachine LearningC++Linux
Requirements
- 8+ years of experience in machine learning systems, software infrastructure, platform engineering, deployment systems, or related large-scale software environments.
- Strong software engineering expertise with hands-on experience in Python and at least one systems programming language such as C++.
- Experience building or operating internal platforms, workflow automation systems, configuration frameworks, release tooling, or developer infrastructure.
- Proven experience integrating machine learning models or complex software components into production environments.
- Strong understanding of software quality, validation, observability, reliability, and production operations.
- Experience developing and debugging software in Linux-based environments.
- Demonstrated success leading technical teams or managing large cross-functional engineering initiatives.
- Strong stakeholder management and communication skills, with the ability to align diverse technical teams around shared goals.
- Experience building self-service engineering platforms that improve speed, consistency, and quality.
- Ability to apply AI-assisted tools and workflows to improve software development and engineering productivity.
- Strong ability to navigate ambiguity, create alignment, and translate complex technical challenges into practical solutions.
Responsibilities
- Lead, mentor, and grow a team of engineers responsible for integrating machine learning models into large-scale production software systems.
- Define the technical roadmap for model integration platforms, focusing on scalability, reliability, automation, and developer productivity.
- Build and improve tools, workflows, and platforms that enable efficient movement of models from development environments into simulation, validation, testing, and production.
- Collaborate with machine learning engineers, infrastructure teams, software platform groups, and technical leaders to ensure effective model integration and deployment.
- Improve model onboarding, configuration management, interface consistency, observability, and release readiness throughout the integration lifecycle.
- Identify opportunities to automate processes, reduce operational bottlenecks, and improve engineering efficiency.
- Establish strong engineering standards around quality, validation, governance, testing, and operational excellence.
- Partner with leadership teams to prioritize initiatives, manage dependencies, and deliver strategic technical milestones.
- Provide coaching, performance feedback, and career development support to engineers while fostering an inclusive and high-performing team culture.
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