About the Role
Design and implement scalable machine learning models and deep learning architectures. Work closely with research and engineering teams to translate prototypes into robust, high-performance systems.
Responsibilities
- Develop and train deep learning models for real-world applications
- Optimize model performance and inference speed
- Collaborate with data scientists to refine algorithms
- Deploy machine learning systems into production environments
- Monitor and maintain model accuracy over time
- Work with large-scale datasets for training and validation
- Improve data pipelines for model training
- Conduct experiments to validate model improvements
- Support integration of AI capabilities into software products
- Troubleshoot issues in model deployment and operation
- Ensure models meet scalability and reliability standards
- Stay current with advancements in deep learning research
- Contribute to technical documentation and system design
- Participate in code reviews and architecture discussions
- Implement security and privacy safeguards in AI systems
Nice to Have
- PhD in a relevant technical discipline
- Experience with reinforcement learning
- Contributions to open-source machine learning projects
- Publications in peer-reviewed AI conferences or journals
- Hands-on experience with edge AI deployment
- Familiarity with model quantization and pruning
- Experience with automated machine learning tools
- Knowledge of adversarial machine learning
- Background in high-performance computing
- Experience mentoring junior engineers
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid work model with flexible remote options
Team
Collaborative AI research and development team
Research Collaboration
- Engage in joint projects with academic and industry research partners
- Contribute to cutting-edge AI innovation through collaborative studies
Technology Stack
- Utilize modern deep learning libraries and GPU-optimized frameworks
- Work with scalable cloud infrastructure and distributed training systems
Available for qualified candidates
