About the Role
The role involves leading the design and implementation of machine learning models, improving system performance, and collaborating with engineering and research teams to deploy AI-driven features into production environments.
Responsibilities
- Design and train deep learning models for real-world applications
- Optimize model inference speed and resource efficiency
- Collaborate with data scientists to refine training datasets
- Deploy machine learning pipelines in cloud environments
- Monitor model performance and implement updates
- Write clean, maintainable, and well-documented code
- Evaluate emerging AI frameworks and tools
- Troubleshoot issues in production AI systems
- Work closely with product teams to define AI requirements
- Ensure models comply with ethical and safety standards
- Conduct code reviews and mentor junior engineers
- Improve model accuracy through iterative experimentation
- Integrate AI capabilities into user-facing applications
- Develop automated testing for machine learning components
- Support data collection and annotation processes
- Maintain version control for models and datasets
- Participate in architecture design discussions
- Contribute to technical documentation
- Scale systems to handle increasing data loads
- Implement security best practices in AI workflows
Nice to Have
- Advanced degree in computer science or machine learning
- Experience with large language models
- Contributions to open-source AI projects
- Publications in AI or machine learning venues
- Knowledge of reinforcement learning techniques
- Experience with edge deployment of AI models
- Leadership experience in technical projects
- Familiarity with regulatory aspects of AI
Compensation
Competitive salary and equity package
Work Arrangement
Fully remote with flexible hours
Team
Small, cross-functional team focused on AI product development
Technology Stack
- Primary languages: Python, JavaScript
- Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
- Cloud: AWS SageMaker, GCP Vertex AI
- DevOps: Docker, Kubernetes, CI/CD pipelines
- Monitoring: Prometheus, Grafana, custom logging
Development Philosophy
- Iterative development with frequent user feedback
- Strong emphasis on code quality and testing
- Data-driven decision making
- Security-first mindset
- Commitment to ethical AI practices
Available for qualified candidates
