About the Role
The role involves developing and refining large language models, implementing machine learning pipelines, and contributing to model deployment and evaluation in production environments.
Responsibilities
- Design and train large language models for specific use cases
- Optimize model performance and inference efficiency
- Collaborate on data curation and preprocessing pipelines
- Evaluate model outputs for accuracy and safety
- Implement scalable training workflows
- Debug and resolve model-related issues
- Contribute to model interpretability and alignment efforts
- Work with engineering teams to integrate models into applications
- Stay current with advancements in natural language processing
- Document model development processes and results
- Participate in peer reviews of machine learning code and design
- Assist in benchmarking against state-of-the-art models
- Support deployment of models in production environments
- Monitor model performance post-deployment
- Collaborate with cross-functional teams on product integration
- Identify opportunities for model improvement
- Ensure compliance with ethical AI guidelines
- Contribute to research initiatives and technical publications
- Assist in building automated testing frameworks for models
- Help define best practices for LLM development
- Work with distributed computing systems for training
- Optimize resource utilization during training runs
- Integrate feedback loops for model refinement
- Support version control and reproducibility of experiments
- Participate in technical planning and roadmap discussions
Nice to Have
- Advanced degree in machine learning or related discipline
- Published research in NLP or deep learning venues
- Hands-on experience with LLM scaling laws
- Experience with model quantization or distillation
- Knowledge of low-rank adaptation methods
- Familiarity with prompt engineering and tuning
- Experience with model serving frameworks
- Background in ethical AI development
- Contributions to open-source ML projects
- Understanding of computational efficiency trade-offs
Compensation
Competitive salary with equity and benefits
Work Arrangement
Hybrid work model with flexible scheduling
Team
Close-knit team of researchers and engineers focused on advancing language model capabilities
Research Collaboration
- Work closely with research scientists to prototype new modeling techniques
- Contribute to interdisciplinary projects involving language understanding
- Engage in knowledge sharing through internal seminars and workshops
Performance Culture
- Focus on measurable impact and continuous improvement
- Emphasis on reproducibility and rigorous experimentation
- Support for innovation within engineering constraints
Available for qualified candidates
