About the Role
The role involves translating cutting-edge natural language processing research into robust, production-ready systems while working closely with interdisciplinary teams.
Responsibilities
- Design and implement machine learning models for language understanding tasks
- Collaborate with researchers to prototype new algorithmic approaches
- Optimize model performance and inference efficiency
- Contribute to large-scale training infrastructure
- Evaluate model outputs using quantitative and qualitative methods
- Write clean, maintainable code for research and production systems
- Debug and resolve technical issues in distributed environments
- Participate in code reviews and technical design discussions
- Support deployment of models into production pipelines
- Monitor model behavior and performance over time
- Assist in refining data collection and annotation processes
- Help scale training workflows across multiple GPUs and nodes
- Ensure reproducibility of experimental results
- Document research findings and technical decisions
- Integrate feedback from product and research teams
- Stay current with advancements in NLP and deep learning
- Contribute to internal tools for experimentation
- Work with large text datasets while maintaining data integrity
- Support ethical evaluation of model outputs
- Collaborate on benchmarking against state-of-the-art systems
- Improve model interpretability and analysis tooling
- Assist in versioning and tracking of model iterations
- Help maintain research codebases and dependencies
- Contribute to technical roadmaps for model development
- Participate in cross-team knowledge sharing
Nice to Have
- Master’s or PhD in computer science or related field
- Published research in NLP, machine learning, or AI conferences
- Experience with transformer-based models
- Contributions to open-source machine learning projects
- Experience deploying models in production environments
- Familiarity with model serving frameworks
- Knowledge of reinforcement learning techniques
- Experience with multilingual language models
- Background in distributed systems design
- Understanding of model compression and quantization
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model available
Team
Collaborative research and engineering environment
What We Value
- Curiosity-driven problem solving
- Rigorous experimental methodology
- Clear technical communication
- Ownership of project outcomes
- Collaborative mindset
Life at the Company
- Flexible work hours
- Learning and development stipend
- Health and wellness benefits
- Remote collaboration tools
- Team offsites and gatherings
Available for qualified candidates
