About the Role
The role involves designing and deploying scalable AI solutions that integrate directly into core product functionality, with emphasis on real-world reliability, performance, and maintainability.
Responsibilities
- Develop and refine machine learning models for production-grade applications
- Collaborate with cross-functional teams to identify high-impact AI use cases
- Optimize model inference speed and accuracy under real-world constraints
- Implement monitoring systems for model performance and data drift
- Translate research concepts into maintainable, testable code
- Work closely with product and design to align AI capabilities with user needs
- Improve data pipelines supporting training and evaluation workflows
- Ensure models meet security and compliance standards
- Lead code reviews and architectural discussions for AI components
- Document technical decisions and system behavior for team scalability
- Troubleshoot production issues related to AI subsystems
- Contribute to best practices for model versioning and deployment
- Evaluate third-party AI tools and libraries for integration potential
- Mentor engineers working on machine learning tasks
- Drive initiatives to improve model explainability and auditability
- Balance innovation with technical debt management in AI systems
- Support integration of AI features across multiple product surfaces
- Participate in defining roadmap priorities for intelligent automation
- Ensure ethical considerations are addressed in model design
- Maintain awareness of advancements in applied AI research
- Deliver reliable systems that function effectively in diverse environments
- Use feedback loops to refine model behavior over time
- Collaborate on defining success metrics for AI-powered features
- Ensure solutions are accessible and inclusive by design
- Contribute to post-mortems and incident reviews involving AI components
Nice to Have
- Master’s degree in computer science, machine learning, or related field
- Experience with large language models or transformer-based architectures
- Background in cybersecurity or trust infrastructure systems
- Contributions to open-source machine learning projects
- Experience working with regulated data or compliance-heavy domains
- Knowledge of model serving frameworks such as TensorFlow Serving or TorchServe
- Familiarity with MLOps platforms like MLflow or Kubeflow
- Published work in AI or software engineering venues
- Experience with edge-case handling in AI systems
- Leadership in cross-team AI initiatives
Compensation
Competitive salary with equity and benefits
Work Arrangement
Hybrid or remote options available
Team
Collaborative engineering team focused on AI-driven product development
Why This Role Matters
AI systems are central to verifying trust and automating compliance workflows. This role directly shapes how intelligent systems interpret evidence, make decisions, and scale assurance across organizations.
Impact You’ll Make
You will build AI features that reduce manual review time, improve detection accuracy, and increase confidence in automated trust signals across digital ecosystems.
Engineering Culture
We value clear technical communication, thoughtful iteration, and systems that work reliably at scale. Engineers are empowered to lead initiatives and improve tooling across the stack.
Growth Opportunities
You’ll have the chance to influence architectural direction, mentor peers, and contribute to long-term AI strategy within the product ecosystem.
Available for qualified candidates
