About the Role
The role involves building and deploying machine learning models, integrating AI capabilities into existing platforms, and improving system intelligence through iterative development and testing.
Responsibilities
- Design and develop machine learning models for production environments
- Collaborate with engineering teams to integrate AI features into software products
- Optimize algorithms for performance, scalability, and accuracy
- Evaluate model outputs using statistical methods and real-world data
- Maintain documentation for model architecture and training pipelines
- Troubleshoot and resolve issues in AI-driven systems
- Stay current with advancements in artificial intelligence and machine learning
- Implement data preprocessing techniques to improve model input quality
- Work with large datasets to train and validate models
- Support deployment of models into cloud-based infrastructure
- Monitor model performance post-deployment and recommend improvements
- Participate in code reviews and technical design discussions
- Ensure compliance with data privacy and security standards
- Develop APIs to serve machine learning models
- Contribute to research initiatives for novel AI applications
- Assist in defining project requirements and timelines
- Use version control systems for model and code management
- Apply software engineering best practices to AI development
- Collaborate with product teams to understand use cases
- Test models under diverse operational conditions
- Refactor legacy systems to incorporate AI functionality
- Work with containerization tools for deployment consistency
- Support automated testing frameworks for AI components
- Engage in cross-functional planning for AI integration
- Provide technical guidance on AI feasibility for new features
Nice to Have
- Master's degree in a technical field
- Experience with reinforcement learning
- Contributions to open-source AI projects
- Publications in machine learning or AI conferences
- Experience with distributed training frameworks
- Knowledge of edge computing for AI
- Background in cybersecurity as it relates to AI systems
- Familiarity with regulatory standards for AI
- Experience mentoring junior developers
- Project leadership in AI initiatives
Compensation
Competitive salary with performance-based bonuses
Work Arrangement
Remote
Team
Collaborative team of engineers and data scientists working across multiple time zones
Technology Stack
- Primary languages: Python, SQL
- Frameworks: TensorFlow, PyTorch, Scikit-learn
- Cloud: Google Cloud Platform, AWS SageMaker
- Deployment: Docker, Kubernetes, REST APIs
- Data tools: Pandas, NumPy, Apache Beam
Work Expectations
- Core hours overlap required across time zones
- Weekly team syncs and sprint planning
- Asynchronous communication preferred
- Documentation standards enforced
- Code quality and testing mandatory
Not available
