Machine Learning Engineer
US$190,000 – US$220,000 / yr base salary
Posted 10 Jun 2026
Advertised as “Senior Machine Learning Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
| Base salary | US$190,000 – US$220,000 / yr |
|---|
Field of studyArtificial Intelligence, Computer Science (or related)
Study levelBachelor's or higher
About AvePoint:
Beyond Secure. AvePoint is the global leader in data security, governance, and resilience, going beyond traditional solutions to ensure a robust data foundation and enable organizations everywhere to collaborate with confidence. Over 25,000 customers worldwide rely on the AvePoint Confidence Platform to prepare, secure, and optimize their critical data across Microsoft, Google, Salesforce, and other collaboration environments. AvePoint’s global channel partner program includes approximately 5,000 managed service providers, value-added resellers, and systems integrators, with our solutions available in more than 100 cloud marketplaces. To learn more, visit www.avepoint.com.
At AvePoint, we are committed to investing in our people. Agility, passion and teamwork set us up to do our best work and foster a culture where you are empowered to craft your career, make an impact, and own (y)our future. Unleash the power of you!
About the role:
We are seeking an analytical and innovative Senior Machine Learning Engineer to join our Data & AI team. You will play a key role in developing and deploying advanced machine learning models to solve real-world business challenges. Your efforts will help optimize data pipelines, extract actionable insights, and shape the future of intelligent solutions at AvePoint.
Key responsibilities:
- Solution Design: Design, build, and deploy machine learning models leveraging structured and unstructured data that align with the overall SaaS product strategy. Collaborate with stakeholders to understand business requirements and translate them into AI-based functionalities. Drive holistic end-to-end AI architecture from data pipeline to LLM to platform components to application integration.
- System Integration: Architect the integration of AI components into the existing SaaS infrastructure, ensuring scalability, reliability, and optimal performance. Collaborate with development teams to implement these integrations effectively.
- Data Management: Design data pipelines and storage solutions for handling large volumes of data required for AI models. Ensure data security, privacy, and compliance with relevant regulations.
- Scalability and Performance Optimization: Architect solutions that can scale horizontally to handle growing user bases. Create and optimize AI algorithms and workflows for speed, efficiency, and resource utilization.
- Security and Compliance: Implement security measures to safeguard AI models and data. Ensure compliance with relevant industry standards and regulations. Develop guidance for the use of AI in product and internal tooling.