Applied ML Scientist

Designing and implementing machine learning models for production systems

Conducting applied research to solve real-world problems using AI and machine learning

Training, fine-tuning, and evaluating large-scale deep learning models

Developing generative AI models and systems including language and vision models

Building end-to-end machine learning pipelines from data to deployment

Designing experiments to validate novel AI techniques and measure their effectiveness

Analyzing and interpreting model performance through rigorous evaluation frameworks and metrics

Translating domain-specific requirements into well-defined machine learning problems

Writing high-quality, production-ready, and well-tested code

Working with large-scale real-world datasets across diverse domains and modalities

Identifying root causes of model failures and developing mitigation strategies

Conducting scaling experiments and optimizing training pipelines for large-scale models

Optimizing models for specific hardware constraints and computational efficiency

Monitoring and debugging machine learning systems in production environments

Creating synthetic data and leveraging domain-specific datasets for model improvement

Applying reinforcement learning techniques to improve model alignment and performance

Building and deploying agentic AI systems that automate complex multi-step workflows

Developing multimodal AI systems that integrate and reason over diverse data types

Applying foundation models and prompt engineering to downstream applications

Implementing retrieval and ranking systems for information discovery and semantic search

Collaborating with cross-functional teams including engineers, product managers, and domain experts

Communicating complex technical research findings to peers and non-technical stakeholders

Translating user feedback and business requirements into technical ML solutions

Publishing research findings and presenting at academic conferences

Mentoring and guiding other researchers and engineers on ML projects