Tasks:
- * Assess and de-risk emerging AI techniques
- * Build LLM-based systems
- * Build model deployment, monitoring, and retraining pipelines
- * Build scalable, high-performance ML products
- * Collaborate with product owners, scientists, engineers, and product managers
- * Design ML experiments
- * Design, train, evaluate, and iterate on machine learning models
- * Develop Agentic AI systems
- * Develop and deploy cloud-based ML and AI solutions
- * Evaluate model performance using metrics and error analysis
- * Fine-tune large language models
- * Implement retrieval-augmented generation
- * Own ML work from design through production monitoring
- * Translate product requirements into measurable ML outcomes
- * Translate research ideas into production ready systems
Perks/Benefits:
- + 401k plan with company match
- + Employee stock purchase plan
- + Family leave programs
- + Flexible or remote work options
- + Flexible spending accounts
- + Flexible time away
- + Health plans
- + Matching donations
Skills/Tech stack required:
[Agentic AI] [Agentic AI Frameworks] [AI frameworks] [Algorithm Design] [Artificial Intelligence] [Cloud Computing] [Deep learning] [Error Analysis] [Experiment design] [Fine Tuning] [Java] [Language Models] [Language Processing] [Large Language Models] [Large-scale] [Large-scale Systems] [LLM Fine-tuning] [Machine Learning] [MLOps] [Model Deployment] [Model Evaluation] [Model Monitoring] [Model performance] [Model Performance Metrics] [Model retraining] [Model Training] [Natural Language] [Natural Language Processing] [Performance Metrics] [Performance Tuning] [Profiling] [Python] [Retrieval-Augmented Generation] [Scale systems] [Software Engineering] [Supervised Learning] [Unit Testing] [Unsupervised Learning]
Educational requirements:
N/A
Role(s):
[Applied Scientist] [Engineer] [Learning Engineer] [Machine Learning Engineer] [Scientist] [Staff Machine Learning Engineer]