Machine Learning Engineer

Building scalable data processing and model training pipelines for production machine learning systems

Designing and optimizing deep learning models for computer vision tasks including detection, segmentation, and tracking

Developing end-to-end machine learning infrastructure spanning data pipelines, training systems, and model serving

Implementing monitoring frameworks and reliability systems for deployed machine learning models in production

Designing data annotation strategies, labeling workflows, and dataset management systems

Optimizing machine learning models for hardware constraints and inference latency requirements

Conducting proof-of-concept experiments and translating research into production prototypes

Debugging functional and performance issues in machine learning systems and frameworks

Testing, validating, and benchmarking machine learning software stacks for correctness and performance

Applying importance sampling and statistical techniques for efficient evaluation and testing

Designing distributed training systems for large-scale foundation models and multi-billion parameter networks

Architecting and scaling multi-agent systems and orchestration frameworks

Building retrieval and ranking systems for search and question-answering applications

Developing reinforcement learning systems and agentic frameworks for automation tasks

Building knowledge graph systems and structured data extraction pipelines

Collaborating with cross-functional teams including research, systems, and product teams

Translating real-world requirements and user behavior into technical specifications and solutions

Championing engineering best practices and code quality in large complex codebases

Leading technical direction and architecture decisions for machine learning systems

Contributing to and maintaining open-source machine learning libraries and frameworks