About the Role
The role involves designing, training, and optimizing large language models and generative AI systems, with emphasis on real-world deployment and ethical considerations.
Compensation
Competitive salary and equity package
Work Arrangement
Remote
Team
Small, interdisciplinary team focused on rapid experimentation and deployment of AI technologies
Responsibilities
- Design and implement scalable training pipelines for large language models
- Optimize model performance and inference efficiency across diverse hardware
- Collaborate with researchers to prototype novel generative AI architectures
- Evaluate model outputs for quality, safety, and alignment with intended use
- Deploy models into production environments with monitoring and feedback loops
- Contribute to data curation and annotation strategies for training sets
- Maintain documentation for model development and deployment processes
- Troubleshoot issues in training or inference workflows
- Stay current with advancements in generative AI and LLM research
- Work closely with engineering and product teams to integrate AI capabilities
Qualifications
- Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow
- Experience with transformer-based models and attention mechanisms
- Strong understanding of deep learning optimization techniques
- Familiarity with distributed training and model parallelism
- Hands-on experience deploying models using containerization and cloud platforms
- Knowledge of natural language processing tasks and benchmarks
- Ability to analyze model behavior and identify failure modes
- Experience with version control and collaborative coding practices
- Strong written and verbal communication skills
- Bachelor’s degree in computer science, engineering, or related field
Preferred Qualifications
- Master’s or PhD in machine learning, artificial intelligence, or related discipline
- Published research in NLP, generative modeling, or AI ethics
- Experience with model quantization or distillation techniques
- Contributions to open-source machine learning projects
- Familiarity with reinforcement learning from human feedback (RLHF)
- Knowledge of data privacy and model security principles
- Experience with large-scale data processing tools
- Understanding of bias mitigation strategies in AI systems
- Track record of delivering AI products to end users
- Ability to mentor junior team members in technical topics
Available for qualified candidates
