About the Role
The role involves designing and implementing machine learning systems that incorporate real-time human input, conducting experiments to improve model performance, and translating research into production-grade software.
Responsibilities
- Design and develop machine learning models that integrate human feedback loops
- Collaborate with research and engineering teams to transition prototypes into scalable systems
- Conduct experiments to evaluate model accuracy and user interaction effectiveness
- Optimize training pipelines for efficiency and reproducibility
- Publish findings in academic venues or internal technical reports
- Work closely with product teams to align research goals with user needs
- Improve data annotation tools and workflows to enhance training quality
- Investigate novel approaches in active learning and interactive machine learning
- Maintain and extend codebases for research reproducibility
- Monitor model performance in production environments
- Contribute to the design of user interfaces that support human-AI collaboration
- Analyze user behavior data to inform model improvements
- Develop evaluation metrics tailored to interactive systems
- Ensure models are robust, interpretable, and ethically sound
- Participate in code reviews and technical design discussions
- Mentor junior researchers and engineers
- Stay current with advancements in machine learning and natural language processing
- Integrate third-party tools and datasets into existing workflows
- Troubleshoot system-level issues across research and deployment stages
- Document methodologies and results for internal and external sharing
Nice to Have
- Postdoctoral or industry research experience
- Contributions to open-source machine learning projects
- Experience with multilingual or low-resource language settings
- Background in cognitive science or human-centered AI
- Prior work with real-time collaborative systems
- Knowledge of model explainability techniques
- Experience in agile research environments
- Familiarity with large-scale data processing frameworks
- Involvement in user study design and execution
- Understanding of model versioning and lifecycle management
Compensation
Competitive salary and equity package
Work Arrangement
Remote with flexible hours
Team
Collaborative research and engineering team focused on advancing machine learning and human-in-the-loop systems
Research Impact
Candidates are expected to contribute to both internal innovation and the broader research community through publications, open-source contributions, or conference participation.
Technology Stack
The team uses Python, PyTorch, Kubernetes, and internal annotation platforms, with infrastructure hosted on major cloud providers.
Collaboration Model
Engineers work in cross-functional squads that include researchers, product managers, and designers to ensure alignment between technical innovation and user needs.
Available for qualified candidates
