About the Role
The role involves building and maintaining machine learning systems that operate in distributed and resource-constrained environments, with a focus on reliability, efficiency, and real-time performance.
Responsibilities
- Develop and optimize machine learning models for production environments
- Collaborate with cross-functional teams to integrate AI solutions into existing platforms
- Monitor model performance and implement improvements over time
- Design data pipelines to support training and inference workflows
- Evaluate new machine learning frameworks and tools for applicability
- Ensure models meet accuracy, latency, and scalability requirements
- Work with edge devices to enable on-device inference where needed
- Troubleshoot issues in model deployment and data flow
- Maintain documentation for models and system architecture
- Support A/B testing and experimentation frameworks
- Implement model versioning and tracking systems
- Contribute to security and privacy practices in machine learning systems
- Refactor legacy code to improve maintainability and performance
- Participate in code reviews and technical design discussions
- Stay current with advancements in machine learning research and techniques
Nice to Have
- Advanced degree in computer science, statistics, or related field
- Experience with MLOps tools and platforms
- Knowledge of reinforcement learning techniques
- Familiarity with time-series forecasting models
- Experience optimizing models for low-latency environments
- Background in embedded systems or constrained hardware
- Contributions to open-source machine learning projects
Compensation
Competitive salary with equity and benefits package
Work Arrangement
Hybrid
Team
Small, agile team focused on rapid prototyping and real-world deployment of AI systems
Technology Stack
- Primary languages include Python and C++
- Frameworks include TensorFlow Lite and PyTorch
- Infrastructure uses Kubernetes and Docker
- Data storage relies on BigQuery and Redis
- Monitoring through Prometheus and Grafana
Impact
- Models directly influence device behavior and user experience
- Work contributes to reducing system latency and improving autonomy
- Solutions are deployed globally across thousands of units
Available for qualified candidates
