Applied ML Scientist

Designing and implementing deep learning model architectures for specific problem domains

Training, fine-tuning, and optimizing machine learning models at scale

Building and deploying machine learning systems from research to production

Designing evaluation metrics, benchmarks, and test sets for model performance assessment

Analyzing and debugging machine learning system behavior in real-world applications

Identifying emerging research directions and implementing state-of-the-art techniques

Working with large-scale, multi-modal datasets including images, text, sensor data, and structured data

Writing production-grade code with testing, documentation, and version control practices

Performing empirical research to measure and understand real-world impact and behavior of AI systems

Managing and integrating multiple heterogeneous data sources and modalities

Optimizing model inference performance and computational efficiency for deployment constraints

Monitoring and maintaining machine learning systems in production environments

Designing and executing A/B tests and experimentation frameworks to validate model improvements

Applying domain knowledge from specialized fields such as biology, physics, or autonomous systems

Developing generative models including language models, vision models, and diffusion-based approaches

Implementing reinforcement learning techniques for model post-training and optimization

Building agentic systems and multi-agent AI workflows for autonomous task execution

Designing reward functions and training pipelines for language model alignment and behavior shaping

Conducting red-teaming and adversarial evaluation to identify AI safety risks and vulnerabilities

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

Translating domain-specific requirements and user feedback into machine learning objectives

Communicating complex technical findings and results to both technical and non-technical stakeholders