About the Role
The role involves owning the machine learning infrastructure for computer vision models, from training pipelines to inference systems, while collaborating with research and product teams to deliver reliable, high-performance solutions.
Responsibilities
- Design and implement scalable training pipelines for computer vision models
- Deploy and monitor machine learning models in production environments
- Optimize inference performance and reduce latency across systems
- Maintain and improve data curation and labeling workflows
- Ensure model reliability through testing, validation, and monitoring
- Collaborate with research teams to transition prototypes into production
- Manage versioning for models, datasets, and training configurations
- Troubleshoot issues in distributed training and inference systems
- Improve system observability with logging, metrics, and alerting
- Automate repetitive operations tasks to increase team efficiency
- Enforce data quality standards across training and evaluation sets
- Scale infrastructure to support growing model complexity and data volume
- Integrate security and access controls into ML workflows
- Support A/B testing and model experimentation frameworks
- Document system architecture and operational procedures
- Respond to incidents and performance degradation in production
- Evaluate new tools and platforms for MLOps improvements
- Ensure compliance with data privacy and retention policies
- Optimize cloud resource usage to control costs
- Work closely with product teams to understand requirements and constraints
- Maintain reproducibility across training runs and deployments
- Implement rollback strategies for failed model updates
- Support edge deployment of models where applicable
- Contribute to internal tooling for data and model management
- Stay current with advancements in computer vision and MLOps
Nice to Have
- Master’s or PhD in computer science, machine learning, or related field
- Experience with real-time video processing systems
- Background in medical imaging or healthcare AI applications
- Contributions to open-source MLOps tools or frameworks
- Experience with edge computing and on-device inference
- Knowledge of regulatory standards for AI in clinical settings
- Familiarity with 3D computer vision techniques
- Experience mentoring junior engineers
- Track record of improving system reliability and uptime
- Hands-on work with large-scale annotated datasets
Compensation
Competitive salary with equity and benefits package
Work Arrangement
Remote with flexible hours
Team
Small, cross-functional team focused on rapid iteration and deployment
Why This Role Matters
This position plays a critical role in bridging the gap between research and real-world application, ensuring that computer vision models perform reliably in production environments. The engineer will directly impact product quality, system scalability, and the speed at which new features are delivered to users.
What You’ll Work On
You’ll build and maintain the infrastructure that powers real-time image analysis, support active learning loops for data improvement, and ensure models adapt to new data without degradation. Projects include optimizing model serving for low-latency environments and creating feedback systems for continuous improvement.
Available for qualified candidates
